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Unsupervised Universal Image Segmentation | Dantong Niu, Xudong Wang, Xinyang Han, Long Lian, Roei Herzig, Trevor Darrell | Several unsupervised image segmentation approaches have been proposed which eliminate the need for dense manually-annotated segmentation masks; current models separately handle either semantic segmentation (e.g. STEGO) or class-agnostic instance segmentation (e.g. CutLER) but not both (i.e. panoptic segmentation). We propose an Unsupervised Universal Segmentation model (U2Seg) adept at performing various image segmentation tasks---instance semantic and panoptic---using a novel unified framework. U2Seg generates pseudo semantic labels for these segmentation tasks via leveraging self-supervised models followed by clustering; each cluster represents different semantic and/or instance membership of pixels. We then self-train the model on these pseudo semantic labels yielding substantial performance gains over specialized methods tailored to each task: a +2.6 APbox boost (vs. CutLER) in unsupervised instance segmentation on COCO and a +7.0 PixelAcc increase (vs. STEGO) in unsupervised semantic segmentation on COCOStuff. Moreover our method sets up a new baseline for unsupervised panoptic segmentation which has not been previously explored. U2Seg is also a strong pretrained model for few-shot segmentation surpassing CutLER by +5.0 APmask when trained on a low-data regime e.g. only 1% COCO labels. We hope our simple yet effective method can inspire more research on unsupervised universal image segmentation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Niu_Unsupervised_Universal_Image_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.17243 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Niu_Unsupervised_Universal_Image_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Niu_Unsupervised_Universal_Image_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Niu_Unsupervised_Universal_Image_CVPR_2024_supplemental.pdf | null |
Rethinking Prior Information Generation with CLIP for Few-Shot Segmentation | Jin Wang, Bingfeng Zhang, Jian Pang, Honglong Chen, Weifeng Liu | Few-shot segmentation remains challenging due to the limitations of its labeling information for unseen classes. Most previous approaches rely on extracting high-level feature maps from the frozen visual encoder to compute the pixel-wise similarity as a key prior guidance for the decoder. However such a prior representation suffers from coarse granularity and poor generalization to new classes since these high-level feature maps have obvious category bias. In this work we propose to replace the visual prior representation with the visual-text alignment capacity to capture more reliable guidance and enhance the model generalization. Specifically we design two kinds of training-free prior information generation strategy that attempts to utilize the semantic alignment capability of the Contrastive Language-Image Pre-training model (CLIP) to locate the target class. Besides to acquire more accurate prior guidance we build a high-order relationship of attention maps and utilize it to refine the initial prior information. Experiments on both the PASCAL-5i and COCO-20i datasets show that our method obtains a clearly substantial improvement and reaches the new state-of-the-art performance. The code is available on the project website. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Rethinking_Prior_Information_Generation_with_CLIP_for_Few-Shot_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.08458 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Rethinking_Prior_Information_Generation_with_CLIP_for_Few-Shot_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Rethinking_Prior_Information_Generation_with_CLIP_for_Few-Shot_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Rethinking_Prior_Information_CVPR_2024_supplemental.pdf | null |
SingularTrajectory: Universal Trajectory Predictor Using Diffusion Model | Inhwan Bae, Young-Jae Park, Hae-Gon Jeon | There are five types of trajectory prediction tasks: deterministic stochastic domain adaptation momentary observation and few-shot. These associated tasks are defined by various factors such as the length of input paths data split and pre-processing methods. Interestingly even though they commonly take sequential coordinates of observations as input and infer future paths in the same coordinates as output designing specialized architectures for each task is still necessary. For the other task generality issues can lead to sub-optimal performances. In this paper we propose SingularTrajectory a diffusion-based universal trajectory prediction framework to reduce the performance gap across the five tasks. The core of SingularTrajectory is to unify a variety of human dynamics representations on the associated tasks. To do this we first build a Singular space to project all types of motion patterns from each task into one embedding space. We next propose an adaptive anchor working in the Singular space. Unlike traditional fixed anchor methods that sometimes yield unacceptable paths our adaptive anchor enables correct anchors which are put into a wrong location based on a traversability map. Finally we adopt a diffusion-based predictor to further enhance the prototype paths using a cascaded denoising process. Our unified framework ensures the generality across various benchmark settings such as input modality and trajectory lengths. Extensive experiments on five public benchmarks demonstrate that SingularTrajectory substantially outperforms existing models highlighting its effectiveness in estimating general dynamics of human movements. Code is publicly available at https://github.com/inhwanbae/SingularTrajectory. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bae_SingularTrajectory_Universal_Trajectory_Predictor_Using_Diffusion_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bae_SingularTrajectory_Universal_Trajectory_Predictor_Using_Diffusion_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bae_SingularTrajectory_Universal_Trajectory_Predictor_Using_Diffusion_Model_CVPR_2024_paper.html | CVPR 2024 | null | null |
Generating Handwritten Mathematical Expressions From Symbol Graphs: An End-to-End Pipeline | Yu Chen, Fei Gao, Yanguang Zhang, Maoying Qiao, Nannan Wang | In this paper we explore a novel challenging generation task i.e. Handwritten Mathematical Expression Generation (HMEG) from symbolic sequences. Since symbolic sequences are naturally graph-structured data we formulate HMEG as a graph-to-image (G2I) generation problem. Unlike the generation of natural images HMEG requires critic layout clarity for synthesizing correct and recognizable formulas but has no real masks available to supervise the learning process. To alleviate this challenge we propose a novel end-to-end G2I generation pipeline (i.e. graph - layout - mask - image) which requires no real masks or nondifferentiable alignment between layouts and masks. Technically to boost the capacity of predicting detailed relations among adjacent symbols we propose a Less-is-More (LiM) learning strategy. In addition we design a differentiable layout refinement module which maps bounding boxes to pixel-level soft masks so as to further alleviate ambiguous layout areas. Our whole model including layout prediction mask refinement and image generation can be jointly optimized in an end-to-end manner. Experimental results show that our model can generate high-quality HME images and outperforms previous generative methods. Besides a series of ablations study demonstrate effectiveness of the proposed techniques. Finally we validate that our generated images promisingly boosts the performance of HME recognition models through data augmentation. Our code and results are available at: https://github.com/AiArt-HDU/HMEG. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Generating_Handwritten_Mathematical_Expressions_From_Symbol_Graphs_An_End-to-End_Pipeline_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Generating_Handwritten_Mathematical_Expressions_From_Symbol_Graphs_An_End-to-End_Pipeline_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Generating_Handwritten_Mathematical_Expressions_From_Symbol_Graphs_An_End-to-End_Pipeline_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Generating_Handwritten_Mathematical_CVPR_2024_supplemental.pdf | null |
A Closer Look at the Few-Shot Adaptation of Large Vision-Language Models | Julio Silva-Rodríguez, Sina Hajimiri, Ismail Ben Ayed, Jose Dolz | Efficient transfer learning (ETL) is receiving increasing attention to adapt large pre-trained language-vision models on downstream tasks with a few labeled samples. While significant progress has been made we reveal that state-of-the-art ETL approaches exhibit strong performance only in narrowly-defined experimental setups and with a careful adjustment of hyperparameters based on a large corpus of labeled samples. In particular we make two interesting and surprising empirical observations. First to outperform a simple Linear Probing baseline these methods require to optimize their hyper-parameters on each target task. And second they typically underperform --sometimes dramatically-- standard zero-shot predictions in the presence of distributional drifts. Motivated by the unrealistic assumptions made in the existing literature i.e. access to a large validation set and case-specific grid-search for optimal hyperparameters we propose a novel approach that meets the requirements of real-world scenarios. More concretely we introduce a CLass-Adaptive linear Probe (CLAP) objective whose balancing term is optimized via an adaptation of the general Augmented Lagrangian method tailored to this context. We comprehensively evaluate CLAP on a broad span of datasets and scenarios demonstrating that it consistently outperforms SoTA approaches while yet being a much more efficient alternative. | https://openaccess.thecvf.com/content/CVPR2024/papers/Silva-Rodriguez_A_Closer_Look_at_the_Few-Shot_Adaptation_of_Large_Vision-Language_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Silva-Rodriguez_A_Closer_Look_at_the_Few-Shot_Adaptation_of_Large_Vision-Language_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Silva-Rodriguez_A_Closer_Look_at_the_Few-Shot_Adaptation_of_Large_Vision-Language_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Silva-Rodriguez_A_Closer_Look_CVPR_2024_supplemental.pdf | null |
Generative Rendering: Controllable 4D-Guided Video Generation with 2D Diffusion Models | Shengqu Cai, Duygu Ceylan, Matheus Gadelha, Chun-Hao Paul Huang, Tuanfeng Yang Wang, Gordon Wetzstein | Traditional 3D content creation tools empower users to bring their imagination to life by giving them direct control over a scene's geometry appearance motion and camera path. Creating computer-generated videos however is a tedious manual process which can be automated by emerging text-to-video diffusion models. Despite great promise video diffusion models are difficult to control hindering users to apply their creativity rather than amplifying it. To address this challenge we present a novel approach that combines the controllability of dynamic 3D meshes with the expressivity and editability of emerging diffusion models. For this purpose our approach takes an animated low-fidelity rendered mesh as input and injects the ground truth correspondence information obtained from the dynamic mesh into various stages of a pre-trained text-to-image generation model to output high-quality and temporally consistent frames. We demonstrate our approach on various examples where motion can be obtained by animating rigged assets or changing the camera path. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cai_Generative_Rendering_Controllable_4D-Guided_Video_Generation_with_2D_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.01409 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_Generative_Rendering_Controllable_4D-Guided_Video_Generation_with_2D_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cai_Generative_Rendering_Controllable_4D-Guided_Video_Generation_with_2D_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cai_Generative_Rendering_Controllable_CVPR_2024_supplemental.pdf | null |
Relightable Gaussian Codec Avatars | Shunsuke Saito, Gabriel Schwartz, Tomas Simon, Junxuan Li, Giljoo Nam | The fidelity of relighting is bounded by both geometry and appearance representations. For geometry both mesh and volumetric approaches have difficulty modeling intricate structures like 3D hair geometry. For appearance existing relighting models are limited in fidelity and often too slow to render in real-time with high-resolution continuous environments. In this work we present Relightable Gaussian Codec Avatars a method to build high-fidelity relightable head avatars that can be animated to generate novel expressions. Our geometry model based on 3D Gaussians can capture 3D-consistent sub-millimeter details such as hair strands and pores on dynamic face sequences. To support diverse materials of human heads such as the eyes skin and hair in a unified manner we present a novel relightable appearance model based on learnable radiance transfer. Together with global illumination-aware spherical harmonics for the diffuse components we achieve real-time relighting with all-frequency reflections using spherical Gaussians. This appearance model can be efficiently relit under both point light and continuous illumination. We further improve the fidelity of eye reflections and enable explicit gaze control by introducing relightable explicit eye models. Our method outperforms existing approaches without compromising real-time performance. We also demonstrate real-time relighting of avatars on a tethered consumer VR headset showcasing the efficiency and fidelity of our avatars. | https://openaccess.thecvf.com/content/CVPR2024/papers/Saito_Relightable_Gaussian_Codec_Avatars_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.03704 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Saito_Relightable_Gaussian_Codec_Avatars_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Saito_Relightable_Gaussian_Codec_Avatars_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Saito_Relightable_Gaussian_Codec_CVPR_2024_supplemental.pdf | null |
Why Not Use Your Textbook? Knowledge-Enhanced Procedure Planning of Instructional Videos | Kumaranage Ravindu Yasas Nagasinghe, Honglu Zhou, Malitha Gunawardhana, Martin Renqiang Min, Daniel Harari, Muhammad Haris Khan | In this paper we explore the capability of an agent to construct a logical sequence of action steps thereby assembling a strategic procedural plan. This plan is crucial for navigating from an initial visual observation to a target visual outcome as depicted in real-life instructional videos. Existing works have attained partial success by extensively leveraging various sources of information available in the datasets such as heavy intermediate visual observations procedural names or natural language step-by-step instructions for features or supervision signals. However the task remains formidable due to the implicit causal constraints in the sequencing of steps and the variability inherent in multiple feasible plans. To tackle these intricacies that previous efforts have overlooked we propose to enhance the agent's capabilities by infusing it with procedural knowledge. This knowledge sourced from training procedure plans and structured as a directed weighted graph equips the agent to better navigate the complexities of step sequencing and its potential variations. We coin our approach KEPP a novel Knowledge-Enhanced Procedure Planning system which harnesses a probabilistic procedural knowledge graph extracted from training data effectively acting as a comprehensive textbook for the training domain. Experimental evaluations across three widely-used datasets under settings of varying complexity reveal that KEPP attains superior state-of-the-art results while requiring only minimal supervision. Code and trained model are available at https://github.com/Ravindu-Yasas-Nagasinghe/KEPP | https://openaccess.thecvf.com/content/CVPR2024/papers/Nagasinghe_Why_Not_Use_Your_Textbook_Knowledge-Enhanced_Procedure_Planning_of_Instructional_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.02782 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Nagasinghe_Why_Not_Use_Your_Textbook_Knowledge-Enhanced_Procedure_Planning_of_Instructional_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Nagasinghe_Why_Not_Use_Your_Textbook_Knowledge-Enhanced_Procedure_Planning_of_Instructional_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Nagasinghe_Why_Not_Use_CVPR_2024_supplemental.pdf | null |
Global and Hierarchical Geometry Consistency Priors for Few-shot NeRFs in Indoor Scenes | Xiaotian Sun, Qingshan Xu, Xinjie Yang, Yu Zang, Cheng Wang | It is challenging for Neural Radiance Fields (NeRFs) in the few-shot setting to reconstruct high-quality novel views and depth maps in 360^\circ outward-facing indoor scenes. The captured sparse views for these scenes usually contain large viewpoint variations. This greatly reduces the potential consistency between views leading NeRFs to degrade a lot in these scenarios. Existing methods usually leverage pretrained depth prediction models to improve NeRFs. However these methods cannot guarantee geometry consistency due to the inherent geometry ambiguity in the pretrained models thus limiting NeRFs' performance. In this work we present P\textsuperscript 2 NeRF to capture global and hierarchical geometry consistency priors from pretrained models thus facilitating few-shot NeRFs in 360^\circ outward-facing indoor scenes. On the one hand we propose a matching-based geometry warm-up strategy to provide global geometry consistency priors for NeRFs. This effectively avoids the overfitting of early training with sparse inputs. On the other hand we propose a group depth ranking loss and ray weight mask regularization based on the monocular depth estimation model. This provides hierarchical geometry consistency priors for NeRFs. As a result our approach can fully leverage the geometry consistency priors from pretrained models and help few-shot NeRFs achieve state-of-the-art performance on two challenging indoor datasets. Our code is released at https://github.com/XT5un/P2NeRF. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_Global_and_Hierarchical_Geometry_Consistency_Priors_for_Few-shot_NeRFs_in_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Global_and_Hierarchical_Geometry_Consistency_Priors_for_Few-shot_NeRFs_in_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Global_and_Hierarchical_Geometry_Consistency_Priors_for_Few-shot_NeRFs_in_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_Global_and_Hierarchical_CVPR_2024_supplemental.pdf | null |
FreeKD: Knowledge Distillation via Semantic Frequency Prompt | Yuan Zhang, Tao Huang, Jiaming Liu, Tao Jiang, Kuan Cheng, Shanghang Zhang | Knowledge distillation (KD) has been applied to various tasks successfully and mainstream methods typically boost the student model via spatial imitation losses. However the consecutive downsamplings induced in the spatial domain of teacher model is a type of corruption hindering the student from analyzing what specific information needs to be imitated which results in accuracy degradation. To better understand the underlying pattern of corrupted feature maps we shift our attention to the frequency domain. During frequency distillation we encounter a new challenge: the low-frequency bands convey general but minimal context while the high are more informative but also introduce noise. Not each pixel within the frequency bands contributes equally to the performance. To address the above problem: (1) We propose the Frequency Prompt plugged into the teacher model absorbing the semantic frequency context during finetuning. (2) During the distillation period a pixel-wise frequency mask is generated via Frequency Prompt to localize those pixel of interests (PoIs) in various frequency bands. Additionally we employ a position-aware relational frequency loss for dense prediction tasks delivering a high-order spatial enhancement to the student model. We dub our Frequency Knowledge Distillation method as FreeKD which determines the optimal localization and extent for the frequency distillation. Extensive experiments demonstrate that FreeKD not only outperforms spatial-based distillation methods consistently on dense prediction tasks (e.g. FreeKD brings 3.8 AP gains for RepPoints-R50 on COCO2017 and 4.55 mIoU gains for PSPNet-R18 on Cityscapes) but also conveys more robustness to the student. Notably we also validate the generalization of our approach on large-scale vision models (e.g. DINO and SAM). | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_FreeKD_Knowledge_Distillation_via_Semantic_Frequency_Prompt_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.12079 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_FreeKD_Knowledge_Distillation_via_Semantic_Frequency_Prompt_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_FreeKD_Knowledge_Distillation_via_Semantic_Frequency_Prompt_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_FreeKD_Knowledge_Distillation_CVPR_2024_supplemental.pdf | null |
Can't Make an Omelette Without Breaking Some Eggs: Plausible Action Anticipation Using Large Video-Language Models | Himangi Mittal, Nakul Agarwal, Shao-Yuan Lo, Kwonjoon Lee | We introduce PlausiVL a large video-language model for anticipating action sequences that are plausible in the real-world. While significant efforts have been made towards anticipating future actions prior approaches do not take into account the aspect of plausibility in an action sequence. To address this limitation we explore the generative capability of a large video-language model in our work and further develop the understanding of plausibility in an action sequence by introducing two objective functions a counterfactual-based plausible action sequence learning loss and a long-horizon action repetition loss. We utilize temporal logical constraints as well as verb-noun action pair logical constraints to create implausible/counterfactual action sequences and use them to train the model with plausible action sequence learning loss. This loss helps the model to differentiate between plausible and not plausible action sequences and also helps the model to learn implicit temporal cues crucial for the task of action anticipation. The long-horizon action repetition loss puts a higher penalty on the actions that are more prone to repetition over a longer temporal window. With this penalization the model is able to generate diverse plausible action sequences. We evaluate our approach on two large-scale datasets Ego4D and EPIC-Kitchens-100 and show improvements on the task of action anticipation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mittal_Cant_Make_an_Omelette_Without_Breaking_Some_Eggs_Plausible_Action_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mittal_Cant_Make_an_Omelette_Without_Breaking_Some_Eggs_Plausible_Action_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mittal_Cant_Make_an_Omelette_Without_Breaking_Some_Eggs_Plausible_Action_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mittal_Cant_Make_an_CVPR_2024_supplemental.pdf | null |
On the Estimation of Image-matching Uncertainty in Visual Place Recognition | Mubariz Zaffar, Liangliang Nan, Julian F. P. Kooij | In Visual Place Recognition (VPR) the pose of a query image is estimated by comparing the image to a map of reference images with known reference poses. As is typical for image retrieval problems a feature extractor maps the query and reference images to a feature space where a nearest neighbor search is then performed. However till recently little attention has been given to quantifying the confidence that a retrieved reference image is a correct match. Highly certain but incorrect retrieval can lead to catastrophic failure of VPR-based localization pipelines. This work compares for the first time the main approaches for estimating the image-matching uncertainty including the traditional retrieval-based uncertainty estimation more recent data-driven aleatoric uncertainty estimation and the compute-intensive geometric verification. We further formulate a simple baseline method "SUE" which unlike the other methods considers the freely-available poses of the reference images in the map. Our experiments reveal that a simple L2-distance between the query and reference descriptors is already a better estimate of image-matching uncertainty than current data-driven approaches. SUE outperforms the other efficient uncertainty estimation methods and its uncertainty estimates complement the computationally expensive geometric verification approach. Future works for uncertainty estimation in VPR should consider the baselines discussed in this work. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zaffar_On_the_Estimation_of_Image-matching_Uncertainty_in_Visual_Place_Recognition_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00546 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zaffar_On_the_Estimation_of_Image-matching_Uncertainty_in_Visual_Place_Recognition_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zaffar_On_the_Estimation_of_Image-matching_Uncertainty_in_Visual_Place_Recognition_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zaffar_On_the_Estimation_CVPR_2024_supplemental.pdf | null |
Mask Grounding for Referring Image Segmentation | Yong Xien Chng, Henry Zheng, Yizeng Han, Xuchong Qiu, Gao Huang | Referring Image Segmentation (RIS) is a challenging task that requires an algorithm to segment objects referred by free-form language expressions. Despite significant progress in recent years most state-of-the-art (SOTA) methods still suffer from considerable language-image modality gap at the pixel and word level. These methods generally 1) rely on sentence-level language features for language-image alignment and 2) lack explicit training supervision for fine-grained visual grounding. Consequently they exhibit weak object-level correspondence between visual and language features. Without well-grounded features prior methods struggle to understand complex expressions that require strong reasoning over relationships among multiple objects especially when dealing with rarely used or ambiguous clauses. To tackle this challenge we introduce a novel Mask Grounding auxiliary task that significantly improves visual grounding within language features by explicitly teaching the model to learn fine-grained correspondence between masked textual tokens and their matching visual objects. Mask Grounding can be directly used on prior RIS methods and consistently bring improvements. Furthermore to holistically address the modality gap we also design a cross-modal alignment loss and an accompanying alignment module. These additions work synergistically with Mask Grounding. With all these techniques our comprehensive approach culminates in MagNet (Mask-grounded Network) an architecture that significantly outperforms prior arts on three key benchmarks (RefCOCO RefCOCO+ and G-Ref) demonstrating our method's effectiveness in addressing current limitations of RIS algorithms. Our code and pre-trained weights will be released. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chng_Mask_Grounding_for_Referring_Image_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.12198 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chng_Mask_Grounding_for_Referring_Image_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chng_Mask_Grounding_for_Referring_Image_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chng_Mask_Grounding_for_CVPR_2024_supplemental.pdf | null |
Single-to-Dual-View Adaptation for Egocentric 3D Hand Pose Estimation | Ruicong Liu, Takehiko Ohkawa, Mingfang Zhang, Yoichi Sato | The pursuit of accurate 3D hand pose estimation stands as a keystone for understanding human activity in the realm of egocentric vision. The majority of existing estimation methods still rely on single-view images as input leading to potential limitations e.g. limited field-of-view and ambiguity in depth. To address these problems adding another camera to better capture the shape of hands is a practical direction. However existing multi-view hand pose estimation methods suffer from two main drawbacks: 1) Requiring multi-view annotations for training which are expensive. 2) During testing the model becomes inapplicable if camera parameters/layout are not the same as those used in training. In this paper we propose a novel Single-to-Dual-view adaptation (S2DHand) solution that adapts a pre-trained single-view estimator to dual views. Compared with existing multi-view training methods 1) our adaptation process is unsupervised eliminating the need for multi-view annotation. 2) Moreover our method can handle arbitrary dual-view pairs with unknown camera parameters making the model applicable to diverse camera settings. Specifically S2DHand is built on certain stereo constraints including pair-wise cross-view consensus and invariance of transformation between both views. These two stereo constraints are used in a complementary manner to generate pseudo-labels allowing reliable adaptation. Evaluation results reveal that S2DHand achieves significant improvements on arbitrary camera pairs under both in-dataset and cross-dataset settings and outperforms existing adaptation methods with leading performance. Project page: https://github.com/ut-vision/S2DHand. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Single-to-Dual-View_Adaptation_for_Egocentric_3D_Hand_Pose_Estimation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.04381 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Single-to-Dual-View_Adaptation_for_Egocentric_3D_Hand_Pose_Estimation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Single-to-Dual-View_Adaptation_for_Egocentric_3D_Hand_Pose_Estimation_CVPR_2024_paper.html | CVPR 2024 | null | null |
Time-Efficient Light-Field Acquisition Using Coded Aperture and Events | Shuji Habuchi, Keita Takahashi, Chihiro Tsutake, Toshiaki Fujii, Hajime Nagahara | We propose a computational imaging method for time-efficient light-field acquisition that combines a coded aperture with an event-based camera. Different from the conventional coded-aperture imaging method our method applies a sequence of coding patterns during a single exposure for an image frame. The parallax information which is related to the differences in coding patterns is recorded as events. The image frame and events all of which are measured in a single exposure are jointly used to computationally reconstruct a light field. We also designed an algorithm pipeline for our method that is end-to-end trainable on the basis of deep optics and compatible with real camera hardware. We experimentally showed that our method can achieve more accurate reconstruction than several other imaging methods with a single exposure. We also developed a hardware prototype with the potential to complete the measurement on the camera within 22 msec and demonstrated that light fields from real 3-D scenes can be obtained with convincing visual quality. Our software and supplementary video are available from our project website. | https://openaccess.thecvf.com/content/CVPR2024/papers/Habuchi_Time-Efficient_Light-Field_Acquisition_Using_Coded_Aperture_and_Events_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.07244 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Habuchi_Time-Efficient_Light-Field_Acquisition_Using_Coded_Aperture_and_Events_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Habuchi_Time-Efficient_Light-Field_Acquisition_Using_Coded_Aperture_and_Events_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Habuchi_Time-Efficient_Light-Field_Acquisition_CVPR_2024_supplemental.pdf | null |
EVS-assisted Joint Deblurring Rolling-Shutter Correction and Video Frame Interpolation through Sensor Inverse Modeling | Rui Jiang, Fangwen Tu, Yixuan Long, Aabhaas Vaish, Bowen Zhou, Qinyi Wang, Wei Zhang, Yuntan Fang, Luis Eduardo Garcia Capel, Bo Mu, Tiejun Dai, Andreas Suess | Event-based Vision Sensors (EVS) gain popularity in enhancing CMOS Image Sensor (CIS) video capture. Nonidealities of EVS such as pixel or readout latency can significantly influence the quality of the enhanced images and warrant dedicated consideration in the design of fusion algorithms. A novel approach for jointly computing deblurred rolling-shutter artifact corrected high-speed videos with frame rates up to 10000 FPS using inherently blurry rolling shutter CIS frames of 120 FPS to 150 FPS in conjunction with EVS data from a hybrid CIS-EVS sensor is presented. EVS pixel latency readout latency and the sensor's refractory period are explicitly incorporated into the measurement model. This inverse function problem is solved on a per-pixel manner using an optimization-based framework. The interpolated images are subsequently processed by a novel refinement network. The proposed method is evaluated using simulated and measured datasets under natural and controlled environments. Extensive experiments show reduced shadowing effect a 4 dB increment in PSNR and a 12% improvement in LPIPS score compared to state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jiang_EVS-assisted_Joint_Deblurring_Rolling-Shutter_Correction_and_Video_Frame_Interpolation_through_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jiang_EVS-assisted_Joint_Deblurring_Rolling-Shutter_Correction_and_Video_Frame_Interpolation_through_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jiang_EVS-assisted_Joint_Deblurring_Rolling-Shutter_Correction_and_Video_Frame_Interpolation_through_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jiang_EVS-assisted_Joint_Deblurring_CVPR_2024_supplemental.pdf | null |
Prompt-Enhanced Multiple Instance Learning for Weakly Supervised Video Anomaly Detection | Junxi Chen, Liang Li, Li Su, Zheng-jun Zha, Qingming Huang | Weakly-supervised Video Anomaly Detection (wVAD) aims to detect frame-level anomalies using only video-level labels in training. Due to the limitation of coarse-grained labels Multi-Instance Learning (MIL) is prevailing in wVAD. However MIL suffers from insufficiency of binary supervision to model diverse abnormal patterns. Besides the coupling between abnormality and its context hinders the learning of clear abnormal event boundary. In this paper we propose prompt-enhanced MIL to detect various abnormal events while ensuring clear event boundaries. Concretely we design the abnormal-aware prompts by using abnormal class annotations together with learnable prompt which can incorporate semantic priors into video features dynamically. The detector can utilize the semantic-rich features to capture diverse abnormal patterns. In addition normal context prompt is introduced to amplify the distinction between abnormality and its context facilitating the generation of clear boundary. With the mutual enhancement of abnormal-aware and normal context prompt the model can construct discriminative representations to detect divergent anomalies without ambiguous event boundaries. Extensive experiments demonstrate our method achieves SOTA performance on three public benchmarks. The code is available at https://github.com/Junxi-Chen/PE-MIL. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Prompt-Enhanced_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Prompt-Enhanced_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Prompt-Enhanced_Multiple_Instance_Learning_for_Weakly_Supervised_Video_Anomaly_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Prompt-Enhanced_Multiple_Instance_CVPR_2024_supplemental.pdf | null |
Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animation | Li Hu | Character Animation aims to generating character videos from still images through driving signals. Currently diffusion models have become the mainstream in visual generation research owing to their robust generative capabilities. However challenges persist in the realm of image-to-video especially in character animation where temporally maintaining consistency with detailed information from character remains a formidable problem. In this paper we leverage the power of diffusion models and propose a novel framework tailored for character animation. To preserve consistency of intricate appearance features from reference image we design ReferenceNet to merge detail features via spatial attention. To ensure controllability and continuity we introduce an efficient pose guider to direct character's movements and employ an effective temporal modeling approach to ensure smooth inter-frame transitions between video frames. By expanding the training data our approach can animate arbitrary characters yielding superior results in character animation compared to other image-to-video methods. Furthermore we evaluate our method on image animation benchmarks achieving state-of-the-art results. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hu_Animate_Anyone_Consistent_and_Controllable_Image-to-Video_Synthesis_for_Character_Animation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17117 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_Animate_Anyone_Consistent_and_Controllable_Image-to-Video_Synthesis_for_Character_Animation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_Animate_Anyone_Consistent_and_Controllable_Image-to-Video_Synthesis_for_Character_Animation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hu_Animate_Anyone_Consistent_CVPR_2024_supplemental.zip | null |
FreeCustom: Tuning-Free Customized Image Generation for Multi-Concept Composition | Ganggui Ding, Canyu Zhao, Wen Wang, Zhen Yang, Zide Liu, Hao Chen, Chunhua Shen | Benefiting from large-scale pre-trained text-to-image (T2I) generative models impressive progress has been achieved in customized image generation which aims to generate user-specified concepts. Existing approaches have extensively focused on single-concept customization and still encounter challenges when it comes to complex scenarios that involve combining multiple concepts. These approaches often require retraining/fine-tuning using a few images leading to time-consuming training processes and impeding their swift implementation. Furthermore the reliance on multiple images to represent a singular concept increases the difficulty of customization. To this end we propose FreeCustom a novel tuning-free method to generate customized images of multi-concept composition based on reference concepts using only one image per concept as input. Specifically we introduce a new multi-reference self-attention (MRSA) mechanism and a weighted mask strategy that enables the generated image to access and focus more on the reference concepts. In addition MRSA leverages our key finding that input concepts are better preserved when providing images with context interactions. Experiments show that our method's produced images are consistent with the given concepts and better aligned with the input text. Our method outperforms or performs on par with other training-based methods in terms of multi-concept composition and single-concept customization but is simpler. Codes can be found \href https://github.com/aim-uofa/FreeCustom here . | https://openaccess.thecvf.com/content/CVPR2024/papers/Ding_FreeCustom_Tuning-Free_Customized_Image_Generation_for_Multi-Concept_Composition_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.13870 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ding_FreeCustom_Tuning-Free_Customized_Image_Generation_for_Multi-Concept_Composition_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ding_FreeCustom_Tuning-Free_Customized_Image_Generation_for_Multi-Concept_Composition_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ding_FreeCustom_Tuning-Free_Customized_CVPR_2024_supplemental.pdf | null |
Non-autoregressive Sequence-to-Sequence Vision-Language Models | Kunyu Shi, Qi Dong, Luis Goncalves, Zhuowen Tu, Stefano Soatto | Sequence-to-sequence vision-language models are showing promise but their applicability is limited by their inference latency due to their autoregressive way of generating predictions. We propose a parallel decoding sequence-to-sequence vision-language model trained with a Query-CTC loss that marginalizes over multiple inference paths in the decoder. This allows us to model the joint distribution of tokens rather than restricting to conditional distribution as in an autoregressive model. The resulting model NARVL achieves performance on-par with its state-of-the-art autoregressive counterpart but is faster at inference time reducing from the linear complexity associated with the sequential generation of tokens to a paradigm of constant time joint inference. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shi_Non-autoregressive_Sequence-to-Sequence_Vision-Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.02249 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shi_Non-autoregressive_Sequence-to-Sequence_Vision-Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shi_Non-autoregressive_Sequence-to-Sequence_Vision-Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shi_Non-autoregressive_Sequence-to-Sequence_Vision-Language_CVPR_2024_supplemental.pdf | null |
MaskINT: Video Editing via Interpolative Non-autoregressive Masked Transformers | Haoyu Ma, Shahin Mahdizadehaghdam, Bichen Wu, Zhipeng Fan, Yuchao Gu, Wenliang Zhao, Lior Shapira, Xiaohui Xie | Recent advances in generative AI have significantly enhanced image and video editing particularly in the context of text prompt control. State-of-the-art approaches predominantly rely on diffusion models to accomplish these tasks. However the computational demands of diffusion-based methods are substantial often necessitating large-scale paired datasets for training and therefore challenging the deployment in real applications. To address these issues this paper breaks down the text-based video editing task into two stages. First we leverage an pre-trained text-to-image diffusion model to simultaneously edit few keyframes in an zero-shot way. Second we introduce an efficient model called MaskINT which is built on non-autoregressive masked generative transformers and specializes in frame interpolation between the edited keyframes using the structural guidance from intermediate frames. Experimental results suggest that our MaskINT achieves comparable performance with diffusion-based methodologies while significantly improve the inference time. This research offers a practical solution for text-based video editing and showcases the potential of non-autoregressive masked generative transformers in this domain. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ma_MaskINT_Video_Editing_via_Interpolative_Non-autoregressive_Masked_Transformers_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.12468 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_MaskINT_Video_Editing_via_Interpolative_Non-autoregressive_Masked_Transformers_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_MaskINT_Video_Editing_via_Interpolative_Non-autoregressive_Masked_Transformers_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ma_MaskINT_Video_Editing_CVPR_2024_supplemental.pdf | null |
Active Prompt Learning in Vision Language Models | Jihwan Bang, Sumyeong Ahn, Jae-Gil Lee | Pre-trained Vision Language Models (VLMs) have demonstrated notable progress in various zero-shot tasks such as classification and retrieval. Despite their performance because improving performance on new tasks requires task-specific knowledge their adaptation is essential. While labels are needed for the adaptation acquiring them is typically expensive. To overcome this challenge active learning a method of achieving a high performance by obtaining labels for a small number of samples from experts has been studied. Active learning primarily focuses on selecting unlabeled samples for labeling and leveraging them to train models. In this study we pose the question "how can the pre-trained VLMs be adapted under the active learning framework?" In response to this inquiry we observe that (1) simply applying a conventional active learning framework to pre-trained VLMs even may degrade performance compared to random selection because of the class imbalance in labeling candidates and (2) the knowledge of VLMs can provide hints for achieving the balance before labeling. Based on these observations we devise a novel active learning framework for VLMs denoted as PCB. To assess the effectiveness of our approach we conduct experiments on seven different real-world datasets and the results demonstrate that PCB surpasses conventional active learning and random sampling methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bang_Active_Prompt_Learning_in_Vision_Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.11178 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bang_Active_Prompt_Learning_in_Vision_Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bang_Active_Prompt_Learning_in_Vision_Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bang_Active_Prompt_Learning_CVPR_2024_supplemental.pdf | null |
Learning Multi-Dimensional Human Preference for Text-to-Image Generation | Sixian Zhang, Bohan Wang, Junqiang Wu, Yan Li, Tingting Gao, Di Zhang, Zhongyuan Wang | Current metrics for text-to-image models typically rely on statistical metrics which inadequately represent the real preference of humans. Although recent work attempts to learn these preferences via human annotated images they reduce the rich tapestry of human preference to a single overall score. However the preference results vary when humans evaluate images with different aspects. Therefore to learn the multi-dimensional human preferences we propose the Multi-dimensional Preference Score (MPS) the first multi-dimensional preference scoring model for the evaluation of text-to-image models. The MPS introduces the preference condition module upon CLIP model to learn these diverse preferences. It is trained based on our Multi-dimensional Human Preference (MHP) Dataset which comprises 918315 human preference choices across four dimensions (i.e. aesthetics semantic alignment detail quality and overall assessment) on 607541 images. The images are generated by a wide range of latest text-to-image models. The MPS outperforms existing scoring methods across 3 datasets in 4 dimensions enabling it a promising metric for evaluating and improving text-to-image generation. The model and dataset will be made publicly available to facilitate future research. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Learning_Multi-Dimensional_Human_Preference_for_Text-to-Image_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.14705 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Learning_Multi-Dimensional_Human_Preference_for_Text-to-Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Learning_Multi-Dimensional_Human_Preference_for_Text-to-Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Learning_Multi-Dimensional_Human_CVPR_2024_supplemental.pdf | null |
ViVid-1-to-3: Novel View Synthesis with Video Diffusion Models | Jeong-gi Kwak, Erqun Dong, Yuhe Jin, Hanseok Ko, Shweta Mahajan, Kwang Moo Yi | Generating novel views of an object from a single image is a challenging task. It requires an understanding of the underlying 3D structure of the object from an image and rendering high-quality spatially consistent new views. While recent methods for view synthesis based on diffusion have shown great progress achieving consistency among various view estimates and at the same time abiding by the desired camera pose remains a critical problem yet to be solved. In this work we demonstrate a strikingly simple method where we utilize a pre-trained video diffusion model to solve this problem. Our key idea is that synthesizing a novel view could be reformulated as synthesizing a video of a camera going around the object of interest---a scanning video---which then allows us to leverage the powerful priors that a video diffusion model would have learned. Thus to perform novel-view synthesis we create a smooth camera trajectory to the target view that we wish to render and denoise using both a view-conditioned diffusion model and a video diffusion model. By doing so we obtain a highly consistent novel view synthesis outperforming the state of the art. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kwak_ViVid-1-to-3_Novel_View_Synthesis_with_Video_Diffusion_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kwak_ViVid-1-to-3_Novel_View_Synthesis_with_Video_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kwak_ViVid-1-to-3_Novel_View_Synthesis_with_Video_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kwak_ViVid-1-to-3_Novel_View_CVPR_2024_supplemental.pdf | null |
Active Object Detection with Knowledge Aggregation and Distillation from Large Models | Dejie Yang, Yang Liu | Accurately detecting active objects undergoing state changes is essential for comprehending human interactions and facilitating decision-making. The existing methods for active object detection (AOD) primarily rely on visual appearance of the objects within input such as changes in size shape and relationship with hands. However these visual changes can be subtle posing challenges particularly in scenarios with multiple distracting no-change instances of the same category. We observe that the state changes are often the result of an interaction being performed upon the object thus propose to use informed priors about object related plausible interactions (including semantics and visual appearance) to provide more reliable cues for AOD. Specifically we propose a knowledge aggregation procedure to integrate the aforementioned informed priors into oracle queries within the teacher decoder offering more object affordance commonsense to locate the active object. To streamline the inference process and reduce extra knowledge inputs we propose a knowledge distillation approach that encourages the student decoder to mimic the detection capabilities of the teacher decoder using the oracle query by replicating its predictions and attention. Our proposed framework achieves state-of-the-art performance on four datasets namely Ego4D Epic-Kitchens MECCANO and 100DOH which demonstrates the effectiveness of our approach in improving AOD. The code and models are available at https://github.com/idejie/KAD.git. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Active_Object_Detection_with_Knowledge_Aggregation_and_Distillation_from_Large_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.12509 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Active_Object_Detection_with_Knowledge_Aggregation_and_Distillation_from_Large_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Active_Object_Detection_with_Knowledge_Aggregation_and_Distillation_from_Large_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_Active_Object_Detection_CVPR_2024_supplemental.pdf | null |
NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental Learning | Mustafa Burak Gurbuz, Jean Michael Moorman, Constantine Dovrolis | Deep neural networks (DNNs) struggle to learn in dynamic settings because they mainly rely on static datasets. Continual learning (CL) aims to overcome this limitation by enabling DNNs to incrementally accumulate knowledge. A widely adopted scenario in CL is class-incremental learning (CIL) where DNNs are required to sequentially learn more classes. Among the various strategies in CL replay methods which revisit previous classes stand out as the only effective ones in CIL. Other strategies such as architectural modifications to segregate information across weights and protect them from change are ineffective in CIL. This is because they need additional information during testing to select the correct network parts to use. In this paper we propose NICE Neurogenesis Inspired Contextual Encoding a replay-free architectural method inspired by adult neurogenesis in the hippocampus. NICE groups neurons in the DNN based on different maturation stages and infers which neurons to use during testing without any additional signal. Through extensive experiments across 6 datasets and 3 architectures we show that NICE performs on par with or often outperforms replay methods. We also make the case that neurons exhibit highly distinctive activation patterns for the classes in which they specialize enabling us to determine when they should be used. The code is available at https://github.com/BurakGurbuz97/NICE. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gurbuz_NICE_Neurogenesis_Inspired_Contextual_Encoding_for_Replay-free_Class_Incremental_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gurbuz_NICE_Neurogenesis_Inspired_Contextual_Encoding_for_Replay-free_Class_Incremental_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gurbuz_NICE_Neurogenesis_Inspired_Contextual_Encoding_for_Replay-free_Class_Incremental_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gurbuz_NICE_Neurogenesis_Inspired_CVPR_2024_supplemental.pdf | null |
Generating Human Motion in 3D Scenes from Text Descriptions | Zhi Cen, Huaijin Pi, Sida Peng, Zehong Shen, Minghui Yang, Shuai Zhu, Hujun Bao, Xiaowei Zhou | Generating human motions from textual descriptions has gained growing research interest due to its wide range of applications. However only a few works consider human-scene interactions together with text conditions which is crucial for visual and physical realism. This paper focuses on the task of generating human motions in 3D indoor scenes given text descriptions of the human-scene interactions. This task presents challenges due to the multimodality nature of text scene and motion as well as the need for spatial reasoning. To address these challenges we propose a new approach that decomposes the complex problem into two more manageable sub-problems: (1) language grounding of the target object and (2) object-centric motion generation. For language grounding of the target object we leverage the power of large language models. For motion generation we design an object-centric scene representation for the generative model to focus on the target object thereby reducing the scene complexity and facilitating the modeling of the relationship between human motions and the object. Experiments demonstrate the better motion quality of our approach compared to baselines and validate our design choices. Code will be available at https://zju3dv.github.io/text_scene_motion. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cen_Generating_Human_Motion_in_3D_Scenes_from_Text_Descriptions_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.07784 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cen_Generating_Human_Motion_in_3D_Scenes_from_Text_Descriptions_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cen_Generating_Human_Motion_in_3D_Scenes_from_Text_Descriptions_CVPR_2024_paper.html | CVPR 2024 | null | null |
Weak-to-Strong 3D Object Detection with X-Ray Distillation | Alexander Gambashidze, Aleksandr Dadukin, Maxim Golyadkin, Maria Razzhivina, Ilya Makarov | This paper addresses the critical challenges of sparsity and occlusion in LiDAR-based 3D object detection. Current methods often rely on supplementary modules or specific architectural designs potentially limiting their applicability to new and evolving architectures. To our knowledge we are the first to propose a versatile technique that seamlessly integrates into any existing framework for 3D Object Detection marking the first instance of Weak-to-Strong generalization in 3D computer vision. We introduce a novel framework X-Ray Distillation with Object-Complete Frames suitable for both supervised and semi-supervised settings that leverages the temporal aspect of point cloud sequences. This method extracts crucial information from both previous and subsequent LiDAR frames creating Object-Complete frames that represent objects from multiple viewpoints thus addressing occlusion and sparsity. Given the limitation of not being able to generate Object-Complete frames during online inference we utilize Knowledge Distillation within a Teacher-Student framework. This technique encourages the strong Student model to emulate the behavior of the weaker Teacher which processes simple and informative Object-Complete frames effectively offering a comprehensive view of objects as if seen through X-ray vision. Our proposed methods surpass state-of-the-art in semi-supervised learning by 1-1.5 mAP and enhance the performance of five established supervised models by 1-2 mAP on standard autonomous driving datasets even with default hyperparameters. Code for Object-Complete frames is available here: https://github.com/sakharok13/X-Ray-Teacher-Patching-Tools. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gambashidze_Weak-to-Strong_3D_Object_Detection_with_X-Ray_Distillation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gambashidze_Weak-to-Strong_3D_Object_Detection_with_X-Ray_Distillation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gambashidze_Weak-to-Strong_3D_Object_Detection_with_X-Ray_Distillation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gambashidze_Weak-to-Strong_3D_Object_CVPR_2024_supplemental.pdf | null |
QDFormer: Towards Robust Audiovisual Segmentation in Complex Environments with Quantization-based Semantic Decomposition | Xiang Li, Jinglu Wang, Xiaohao Xu, Xiulian Peng, Rita Singh, Yan Lu, Bhiksha Raj | Audiovisual segmentation (AVS) is a challenging task that aims to segment visual objects in videos according to their associated acoustic cues. With multiple sound sources and background disturbances involved establishing robust correspondences between audio and visual contents poses unique challenges due to (1) complex entanglement across sound sources and (2) frequent changes in the occurrence of distinct sound events. Assuming sound events occur independently the multi-source semantic space can be represented as the Cartesian product of single-source sub-spaces. We are motivated to decompose the multi-source audio semantics into single-source semantics for more effective interactions with visual content. We propose a semantic decomposition method based on product quantization where the multi-source semantics can be decomposed and represented by several disentangled and noise-suppressed single-source semantics. Furthermore we introduce a global-to-local quantization mechanism which distills knowledge from stable global (clip-level) features into local (frame-level) ones to handle frequent changes in audio semantics. Extensive experiments demonstrate that our semantically decomposed audio representation significantly improves AVS performance eg +21.2% mIoU on the challenging AVS-Semantic benchmark with ResNet50 backbone. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_QDFormer_Towards_Robust_Audiovisual_Segmentation_in_Complex_Environments_with_Quantization-based_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.00132 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_QDFormer_Towards_Robust_Audiovisual_Segmentation_in_Complex_Environments_with_Quantization-based_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_QDFormer_Towards_Robust_Audiovisual_Segmentation_in_Complex_Environments_with_Quantization-based_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_QDFormer_Towards_Robust_CVPR_2024_supplemental.zip | null |
Active Open-Vocabulary Recognition: Let Intelligent Moving Mitigate CLIP Limitations | Lei Fan, Jianxiong Zhou, Xiaoying Xing, Ying Wu | Active recognition which allows intelligent agents to explore observations for better recognition performance serves as a prerequisite for various embodied AI tasks such as grasping navigation and room arrangements. Given the evolving environment and the multitude of object classes it is impractical to include all possible classes during the training stage. In this paper we aim at advancing active open-vocabulary recognition empowering embodied agents to actively perceive and classify arbitrary objects. However directly adopting recent open-vocabulary classification models like Contrastive Language Image Pretraining (CLIP) poses its unique challenges. Specifically we observe that CLIP's performance is heavily affected by the viewpoint and occlusions compromising its reliability in unconstrained embodied perception scenarios. Further the sequential nature of observations in agent-environment interactions necessitates an effective method for integrating features that maintains discriminative strength for open-vocabulary classification. To address these issues we introduce a novel agent for active open-vocabulary recognition. The proposed method leverages inter-frame and inter-concept similarities to navigate agent movements and to fuse features without relying on class-specific knowledge. Compared to baseline CLIP model with 29.6% accuracy on ShapeNet dataset the proposed agent could achieve 53.3% accuracy for open-vocabulary recognition without any fine-tuning to the equipped CLIP model. Additional experiments conducted with the Habitat simulator further affirm the efficacy of our method. | https://openaccess.thecvf.com/content/CVPR2024/papers/Fan_Active_Open-Vocabulary_Recognition_Let_Intelligent_Moving_Mitigate_CLIP_Limitations_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17938 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_Active_Open-Vocabulary_Recognition_Let_Intelligent_Moving_Mitigate_CLIP_Limitations_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_Active_Open-Vocabulary_Recognition_Let_Intelligent_Moving_Mitigate_CLIP_Limitations_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Fan_Active_Open-Vocabulary_Recognition_CVPR_2024_supplemental.zip | null |
Backdoor Defense via Test-Time Detecting and Repairing | Jiyang Guan, Jian Liang, Ran He | Deep neural networks have played a crucial part in many critical domains such as autonomous driving face recognition and medical diagnosis. However deep neural networks are facing security threats from backdoor attacks and can be manipulated into attacker-decided behaviors by the backdoor attacker. To defend the backdoor prior research has focused on using clean data to remove backdoor attacks before model deployment. In this paper we investigate the possibility of defending against backdoor attacks by utilizing test-time partially poisoned data to remove the backdoor from the model. To address the problem a two-stage method TTBD is proposed. In the first stage we propose a backdoor sample detection method DDP to identify poisoned samples from a batch of mixed partially poisoned samples. Once the poisoned samples are detected we employ Shapley estimation to calculate the contribution of each neuron's significance in the network locate the poisoned neurons and prune them to remove backdoor in the models. Our experiments demonstrate that TTBD removes the backdoor successfully with only a batch of partially poisoned data across different model architectures and datasets against different types of backdoor attacks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Guan_Backdoor_Defense_via_Test-Time_Detecting_and_Repairing_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Guan_Backdoor_Defense_via_Test-Time_Detecting_and_Repairing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Guan_Backdoor_Defense_via_Test-Time_Detecting_and_Repairing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Guan_Backdoor_Defense_via_CVPR_2024_supplemental.pdf | null |
Fast Adaptation for Human Pose Estimation via Meta-Optimization | Shengxiang Hu, Huaijiang Sun, Bin Li, Dong Wei, Weiqing Li, Jianfeng Lu | Domain shift is a challenge for supervised human pose estimation where the source data and target data come from different distributions. This is why pose estimation methods generally perform worse on the test set than on the training set. Recently test-time adaptation has proven to be an effective way to deal with domain shift in human pose estimation. Although the performance on the target domain has been improved existing methods require a large number of weight updates for convergence which is time-consuming and brings catastrophic forgetting. To solve these issues we propose a meta-auxiliary learning method to achieve fast adaptation for domain shift during inference. Specifically we take human pose estimation as the supervised primary task and propose body-specific image inpainting as a self-supervised auxiliary task. First we jointly train the primary and auxiliary tasks to get a pre-trained model on the source domain. Then meta-training correlates the performance of the two tasks to learn a good weight initialization. Finally meta-testing adapts the meta-learned model to the target data through self-supervised learning. Benefiting from the meta-learning paradigm the proposed method enables fast adaptation to the target domain while preserving the source domain knowledge. The carefully designed auxiliary task better pays attention to human-related semantics in a single image. Extensive experiments demonstrate the effectiveness of our test-time fast adaptation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hu_Fast_Adaptation_for_Human_Pose_Estimation_via_Meta-Optimization_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_Fast_Adaptation_for_Human_Pose_Estimation_via_Meta-Optimization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_Fast_Adaptation_for_Human_Pose_Estimation_via_Meta-Optimization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hu_Fast_Adaptation_for_CVPR_2024_supplemental.pdf | null |
Efficient Meshflow and Optical Flow Estimation from Event Cameras | Xinglong Luo, Ao Luo, Zhengning Wang, Chunyu Lin, Bing Zeng, Shuaicheng Liu | In this paper we explore the problem of event-based meshflow estimation a novel task that involves predicting a spatially smooth sparse motion field from event cameras. To start we generate a large-scale High-Resolution Event Meshflow (HREM) dataset which showcases its superiority by encompassing the merits of high resolution at 1280x720 handling dynamic objects and complex motion patterns and offering both optical flow and meshflow labels. These aspects have not been fully explored in previous works. Besides we propose Efficient Event-based MeshFlow (EEMFlow) network a lightweight model featuring a specially crafted encoder-decoder architecture to facilitate swift and accurate meshflow estimation. Furthermore we upgrade EEMFlow network to support dense event optical flow in which a Confidence-induced Detail Completion (CDC) module is proposed to preserve sharp motion boundaries. We conduct comprehensive experiments to show the exceptional performance and runtime efficiency (39x faster) of our EEMFlow model compared to recent state-of-the-art flow methods. Our code is available at https://github.com/boomluo02/EEMFlow. | https://openaccess.thecvf.com/content/CVPR2024/papers/Luo_Efficient_Meshflow_and_Optical_Flow_Estimation_from_Event_Cameras_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_Efficient_Meshflow_and_Optical_Flow_Estimation_from_Event_Cameras_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_Efficient_Meshflow_and_Optical_Flow_Estimation_from_Event_Cameras_CVPR_2024_paper.html | CVPR 2024 | null | null |
Visual Program Distillation: Distilling Tools and Programmatic Reasoning into Vision-Language Models | Yushi Hu, Otilia Stretcu, Chun-Ta Lu, Krishnamurthy Viswanathan, Kenji Hata, Enming Luo, Ranjay Krishna, Ariel Fuxman | Solving complex visual tasks such as "Who invented the musical instrument on the right?" involves a composition of skills: understanding space recognizing instruments and also retrieving prior knowledge. Recent work shows promise by decomposing such tasks using a large language model (LLM) into an executable program that invokes specialized vision models. However generated programs are error-prone: they omit necessary steps include spurious ones and are unable to recover when the specialized models give incorrect outputs. Moreover they require loading multiple models incurring high latency and computation costs. We propose Visual Program Distillation (VPD) an instruction-tuning framework that produces a vision-language model (VLM) capable of solving complex visual tasks with a single forward pass. VPD distills the reasoning ability of LLMs by using them to sample multiple candidate programs which are then executed and verified to identify the correct one. It translates each correct program into a language description of the reasoning steps which are then distilled into a VLM. Extensive experiments show that VPD improves the VLM's ability to count understand spatial relations and reason compositionally. Our VPD-trained PaLI-X outperforms all prior VLMs achieving state-of-the-art performance across complex vision tasks including MMBench OK-VQA A-OKVQA TallyQA POPE and Hateful Memes. An evaluation with human annotators also confirms that VPD improves model response factuality and consistency. Finally experiments on content moderation demonstrate that VPD is also helpful for adaptation to real-world applications with limited data. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hu_Visual_Program_Distillation_Distilling_Tools_and_Programmatic_Reasoning_into_Vision-Language_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.03052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_Visual_Program_Distillation_Distilling_Tools_and_Programmatic_Reasoning_into_Vision-Language_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_Visual_Program_Distillation_Distilling_Tools_and_Programmatic_Reasoning_into_Vision-Language_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hu_Visual_Program_Distillation_CVPR_2024_supplemental.pdf | null |
OneFormer3D: One Transformer for Unified Point Cloud Segmentation | Maxim Kolodiazhnyi, Anna Vorontsova, Anton Konushin, Danila Rukhovich | Semantic instance and panoptic segmentation of 3D point clouds have been addressed using task-specific models of distinct design. Thereby the similarity of all segmentation tasks and the implicit relationship between them have not been utilized effectively. This paper presents a unified simple and effective model addressing all these tasks jointly. The model named OneFormer3D performs instance and semantic segmentation consistently using a group of learnable kernels where each kernel is responsible for generating a mask for either an instance or a semantic category. These kernels are trained with a transformer-based decoder with unified instance and semantic queries passed as an input. Such a design enables training a model end-to-end in a single run so that it achieves top performance on all three segmentation tasks simultaneously. Specifically our OneFormer3D ranks 1st and sets a new state-of-the-art (+2.1 mAP50) in the ScanNet test leaderboard. We also demonstrate the state-of-the-art results in semantic instance and panoptic segmentation of ScanNet (+21 PQ) ScanNet200 (+3.8 mAP50) and S3DIS (+0.8 mIoU) datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kolodiazhnyi_OneFormer3D_One_Transformer_for_Unified_Point_Cloud_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.14405 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kolodiazhnyi_OneFormer3D_One_Transformer_for_Unified_Point_Cloud_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kolodiazhnyi_OneFormer3D_One_Transformer_for_Unified_Point_Cloud_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kolodiazhnyi_OneFormer3D_One_Transformer_CVPR_2024_supplemental.pdf | null |
JRDB-Social: A Multifaceted Robotic Dataset for Understanding of Context and Dynamics of Human Interactions Within Social Groups | Simindokht Jahangard, Zhixi Cai, Shiki Wen, Hamid Rezatofighi | Understanding human social behaviour is crucial in computer vision and robotics. Micro-level observations like individual actions fall short necessitating a comprehensive approach that considers individual behaviour intra-group dynamics and social group levels for a thorough understanding. To address dataset limitations this paper introduces JRDB-Social an extension of JRDB. Designed to fill gaps in human understanding across diverse indoor and outdoor social contexts JRDB-Social provides annotations at three levels: individual attributes intra-group interactions and social group context. This dataset aims to enhance our grasp of human social dynamics for robotic applications. Utilizing the recent cutting-edge multi-modal large language models we evaluated our benchmark to explore their capacity to decipher social human behaviour. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jahangard_JRDB-Social_A_Multifaceted_Robotic_Dataset_for_Understanding_of_Context_and_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jahangard_JRDB-Social_A_Multifaceted_Robotic_Dataset_for_Understanding_of_Context_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jahangard_JRDB-Social_A_Multifaceted_Robotic_Dataset_for_Understanding_of_Context_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jahangard_JRDB-Social_A_Multifaceted_CVPR_2024_supplemental.zip | null |
A Backpack Full of Skills: Egocentric Video Understanding with Diverse Task Perspectives | Simone Alberto Peirone, Francesca Pistilli, Antonio Alliegro, Giuseppe Averta | Human comprehension of a video stream is naturally broad: in a few instants we are able to understand what is happening the relevance and relationship of objects and forecast what will follow in the near future everything all at once. We believe that - to effectively transfer such an holistic perception to intelligent machines - an important role is played by learning to correlate concepts and to abstract knowledge coming from different tasks to synergistically exploit them when learning novel skills. To accomplish this we look for a unified approach to video understanding which combines shared temporal modelling of human actions with minimal overhead to support multiple downstream tasks and enable cooperation when learning novel skills. We then propose EgoPack a solution that creates a collection of task perspectives that can be carried across downstream tasks and used as a potential source of additional insights as a backpack of skills that a robot can carry around and use when needed. We demonstrate the effectiveness and efficiency of our approach on four Ego4D benchmarks outperforming current state-of-the-art methods. Project webpage: https://sapeirone.github.io/EgoPack. | https://openaccess.thecvf.com/content/CVPR2024/papers/Peirone_A_Backpack_Full_of_Skills_Egocentric_Video_Understanding_with_Diverse_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.03037 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Peirone_A_Backpack_Full_of_Skills_Egocentric_Video_Understanding_with_Diverse_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Peirone_A_Backpack_Full_of_Skills_Egocentric_Video_Understanding_with_Diverse_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Peirone_A_Backpack_Full_CVPR_2024_supplemental.pdf | null |
WOUAF: Weight Modulation for User Attribution and Fingerprinting in Text-to-Image Diffusion Models | Changhoon Kim, Kyle Min, Maitreya Patel, Sheng Cheng, Yezhou Yang | The rapid advancement of generative models facilitating the creation of hyper-realistic images from textual descriptions has concurrently escalated critical societal concerns such as misinformation. Although providing some mitigation traditional fingerprinting mechanisms fall short in attributing responsibility for the malicious use of synthetic images. This paper introduces a novel approach to model fingerprinting that assigns responsibility for the generated images thereby serving as a potential countermeasure to model misuse. Our method modifies generative models based on each user's unique digital fingerprint imprinting a unique identifier onto the resultant content that can be traced back to the user. This approach incorporating fine-tuning into Text-to-Image (T2I) tasks using the Stable Diffusion Model demonstrates near-perfect attribution accuracy with a minimal impact on output quality. Through extensive evaluation we show that our method outperforms baseline methods with an average improvement of 11% in handling image post-processes. Our method presents a promising and novel avenue for accountable model distribution and responsible use. Our code is available in https://github.com/kylemin/WOUAF. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_WOUAF_Weight_Modulation_for_User_Attribution_and_Fingerprinting_in_Text-to-Image_CVPR_2024_paper.pdf | http://arxiv.org/abs/2306.04744 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_WOUAF_Weight_Modulation_for_User_Attribution_and_Fingerprinting_in_Text-to-Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_WOUAF_Weight_Modulation_for_User_Attribution_and_Fingerprinting_in_Text-to-Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_WOUAF_Weight_Modulation_CVPR_2024_supplemental.pdf | null |
Visual In-Context Prompting | Feng Li, Qing Jiang, Hao Zhang, Tianhe Ren, Shilong Liu, Xueyan Zou, Huaizhe Xu, Hongyang Li, Jianwei Yang, Chunyuan Li, Lei Zhang, Jianfeng Gao | In-context prompting in large language models (LLMs) has become a prevalent approach to improve zero-shot capabilities but this idea is less explored in the vision domain. Existing visual prompting methods focus on referring segmentation to segment the most relevant object falling short of addressing many generic vision tasks like open-set segmentation and detection. In this paper we introduce a universal visual in-context prompting framework for both tasks as shown in Fig.1. In particular we build on top of an encoder-decoder architecture and develop a versatile prompt encoder to support a variety of prompts like strokes boxes and points. We further enhance it to take an arbitrary number of reference image segments as the context. Our extensive explorations show that the proposed visual in-context prompting elicits extraordinary referring and generic segmentation capabilities to refer and detect yielding competitive performance to close-set in-domain datasets and showing promising results on many open-set segmentation datasets. By joint training on COCO and SA-1B DINOv achieves 57.7 PQ on COCO and 23.2 PQ on ADE20K. Code will be available at https://github.com/UX-Decoder/DINOv | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Visual_In-Context_Prompting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.13601 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Visual_In-Context_Prompting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Visual_In-Context_Prompting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Visual_In-Context_Prompting_CVPR_2024_supplemental.pdf | null |
Text-Conditioned Generative Model of 3D Strand-based Human Hairstyles | Vanessa Sklyarova, Egor Zakharov, Otmar Hilliges, Michael J. Black, Justus Thies | We present HAAR a new strand-based generative model for 3D human hairstyles. Specifically based on textual inputs HAAR produces 3D hairstyles that could be used as production-level assets in modern computer graphics engines. Current AI-based generative models take advantage of powerful 2D priors to reconstruct 3D content in the form of point clouds meshes or volumetric functions. However by using the 2D priors they are intrinsically limited to only recovering the visual parts. Highly occluded hair structures can not be reconstructed with those methods and they only model the "outer shell" which is not ready to be used in physics-based rendering or simulation pipelines. In contrast we propose a first text-guided generative method that uses 3D hair strands as an underlying representation. Leveraging 2D visual question-answering (VQA) systems we automatically annotate synthetic hair models that are generated from a small set of artist-created hairstyles. This allows us to train a latent diffusion model that operates in a common hairstyle UV space. In qualitative and quantitative studies we demonstrate the capabilities of the proposed model and compare it to existing hairstyle generation approaches. For results please refer to our project page https://haar.is.tue.mpg.de/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sklyarova_Text-Conditioned_Generative_Model_of_3D_Strand-based_Human_Hairstyles_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.11666 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sklyarova_Text-Conditioned_Generative_Model_of_3D_Strand-based_Human_Hairstyles_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sklyarova_Text-Conditioned_Generative_Model_of_3D_Strand-based_Human_Hairstyles_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sklyarova_Text-Conditioned_Generative_Model_CVPR_2024_supplemental.pdf | null |
GPT-4V(ision) is a Human-Aligned Evaluator for Text-to-3D Generation | Tong Wu, Guandao Yang, Zhibing Li, Kai Zhang, Ziwei Liu, Leonidas Guibas, Dahua Lin, Gordon Wetzstein | Despite recent advances in text-to-3D generative methods there is a notable absence of reliable evaluation metrics. Existing metrics usually focus on a single criterion each such as how well the asset aligned with the input text. These metrics lack the flexibility to generalize to different evaluation criteria and might not align well with human preferences. Conducting user preference studies is an alternative that offers both adaptability and human-aligned results. User studies however can be very expensive to scale. This paper presents an automatic versatile and human-aligned evaluation metric for text-to-3D generative models. To this end we first develop a prompt generator using GPT-4V to generate evaluating prompts which serve as input to compare text-to-3D models. We further design a method instructing GPT-4V to compare two 3D assets according to user-defined criteria. Finally we use these pairwise comparison results to assign these models Elo ratings. Experimental results suggest our metric strongly align with human preference across different evaluation criteria. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_GPT-4Vision_is_a_Human-Aligned_Evaluator_for_Text-to-3D_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.04092 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_GPT-4Vision_is_a_Human-Aligned_Evaluator_for_Text-to-3D_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_GPT-4Vision_is_a_Human-Aligned_Evaluator_for_Text-to-3D_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_GPT-4Vision_is_a_CVPR_2024_supplemental.pdf | null |
NTO3D: Neural Target Object 3D Reconstruction with Segment Anything | Xiaobao Wei, Renrui Zhang, Jiarui Wu, Jiaming Liu, Ming Lu, Yandong Guo, Shanghang Zhang | Neural 3D reconstruction from multi-view images has recently attracted increasing attention from the community. Existing methods normally learn a neural field for the whole scene while it is still under-explored how to reconstruct a target object indicated by users. Considering the Segment Anything Model (SAM) has shown effectiveness in segmenting any 2D images in this paper we propose NTO3D a novel high-quality Neural Target Object 3D (NTO3D) reconstruction method which leverages the benefits of both neural field and SAM. We first propose a novel strategy to lift the multi-view 2D segmentation masks of SAM into a unified 3D occupancy field. The 3D occupancy field is then projected into 2D space and generates the new prompts for SAM. This process is iterative until convergence to separate the target object from the scene. After this we then lift the 2D features of the SAM encoder into a 3D feature field in order to improve the reconstruction quality of the target object. NTO3D lifts the 2D masks and features of SAM into the 3D neural field for high-quality neural target object 3D reconstruction. We conduct detailed experiments on several benchmark datasets to demonstrate the advantages of our method. The code will be available at: https://github.com/ucwxb/NTO3D. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wei_NTO3D_Neural_Target_Object_3D_Reconstruction_with_Segment_Anything_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.12790 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wei_NTO3D_Neural_Target_Object_3D_Reconstruction_with_Segment_Anything_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wei_NTO3D_Neural_Target_Object_3D_Reconstruction_with_Segment_Anything_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wei_NTO3D_Neural_Target_CVPR_2024_supplemental.pdf | null |
Instruct-ReID: A Multi-purpose Person Re-identification Task with Instructions | Weizhen He, Yiheng Deng, Shixiang Tang, Qihao Chen, Qingsong Xie, Yizhou Wang, Lei Bai, Feng Zhu, Rui Zhao, Wanli Ouyang, Donglian Qi, Yunfeng Yan | Human intelligence can retrieve any person according to both visual and language descriptions. However the current computer vision community studies specific person re-identification (ReID) tasks in different scenarios separately which limits the applications in the real world. This paper strives to resolve this problem by proposing a new instruct-ReID task that requires the model to retrieve images according to the given image or language instructions. Our instruct-ReID is a more general ReID setting where existing 6 ReID tasks can be viewed as special cases by designing different instructions. We propose a large-scale OmniReID benchmark and an adaptive triplet loss as a baseline method to facilitate research in this new setting. Experimental results show that the proposed multi-purpose ReID model trained on our OmniReID benchmark without finetuning can improve +0.5% +0.6% +7.7% mAP on Market1501 MSMT17 CUHK03 for traditional ReID +6.4% +7.1% +11.2% mAP on PRCC VC-Clothes LTCC for clothes-changing ReID +11.7% mAP on COCAS+ real2 for clothes template based clothes-changing ReID when using only RGB images +24.9% mAP on COCAS+ real2 for our newly defined language-instructed ReID +4.3% on LLCM for visible-infrared ReID +2.6% on CUHK-PEDES for text-to-image ReID. The datasets the model and code are available at https://github.com/hwz-zju/Instruct-ReID. | https://openaccess.thecvf.com/content/CVPR2024/papers/He_Instruct-ReID_A_Multi-purpose_Person_Re-identification_Task_with_Instructions_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/He_Instruct-ReID_A_Multi-purpose_Person_Re-identification_Task_with_Instructions_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/He_Instruct-ReID_A_Multi-purpose_Person_Re-identification_Task_with_Instructions_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/He_Instruct-ReID_A_Multi-purpose_CVPR_2024_supplemental.pdf | null |
OmniMedVQA: A New Large-Scale Comprehensive Evaluation Benchmark for Medical LVLM | Yutao Hu, Tianbin Li, Quanfeng Lu, Wenqi Shao, Junjun He, Yu Qiao, Ping Luo | Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in various multimodal tasks. However their potential in the medical domain remains largely unexplored. A significant challenge arises from the scarcity of diverse medical images spanning various modalities and anatomical regions which is essential in real-world medical applications. To solve this problem in this paper we introduce OmniMedVQA a novel comprehensive medical Visual Question Answering (VQA) benchmark. This benchmark is collected from 73 different medical datasets including 12 different modalities and covering more than 20 distinct anatomical regions. Importantly all images in this benchmark are sourced from authentic medical scenarios ensuring alignment with the requirements of the medical field and suitability for evaluating LVLMs. Through our extensive experiments we have found that existing LVLMs struggle to address these medical VQA problems effectively. Moreover what surprises us is that medical-specialized LVLMs even exhibit inferior performance to those general-domain models calling for a more versatile and robust LVLM in the biomedical field. The evaluation results not only reveal the current limitations of LVLM in understanding real medical images but also highlight our dataset's significance. Our code with dataset are available at https://github.com/OpenGVLab/Multi-Modality-Arena. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hu_OmniMedVQA_A_New_Large-Scale_Comprehensive_Evaluation_Benchmark_for_Medical_LVLM_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.09181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_OmniMedVQA_A_New_Large-Scale_Comprehensive_Evaluation_Benchmark_for_Medical_LVLM_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_OmniMedVQA_A_New_Large-Scale_Comprehensive_Evaluation_Benchmark_for_Medical_LVLM_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hu_OmniMedVQA_A_New_CVPR_2024_supplemental.pdf | null |
Skeleton-in-Context: Unified Skeleton Sequence Modeling with In-Context Learning | Xinshun Wang, Zhongbin Fang, Xia Li, Xiangtai Li, Chen Chen, Mengyuan Liu | In-context learning provides a new perspective for multi-task modeling for vision and NLP. Under this setting the model can perceive tasks from prompts and accomplish them without any extra task-specific head predictions or model fine-tuning. However skeleton sequence modeling via in-context learning remains unexplored. Directly applying existing in-context models from other areas onto skeleton sequences fails due to the similarity between inter-frame and cross-task poses which makes it exceptionally hard to perceive the task correctly from a subtle context. To address this challenge we propose Skeleton-in-Context (SiC) an effective framework for in-context skeleton sequence modeling. Our SiC is able to handle multiple skeleton-based tasks simultaneously after a single training process and accomplish each task from context according to the given prompt. It can further generalize to new unseen tasks according to customized prompts. To facilitate context perception we additionally propose a task-unified prompt which adaptively learns tasks of different natures such as partial joint-level generation sequence-level prediction or 2D-to-3D motion prediction. We conduct extensive experiments to evaluate the effectiveness of our SiC on multiple tasks including motion prediction pose estimation joint completion and future pose estimation. We also evaluate its generalization capability on unseen tasks such as motion-in-between. These experiments show that our model achieves state-of-the-art multi-task performance and even outperforms single-task methods on certain tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Skeleton-in-Context_Unified_Skeleton_Sequence_Modeling_with_In-Context_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Skeleton-in-Context_Unified_Skeleton_Sequence_Modeling_with_In-Context_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Skeleton-in-Context_Unified_Skeleton_Sequence_Modeling_with_In-Context_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Skeleton-in-Context_Unified_Skeleton_CVPR_2024_supplemental.pdf | null |
DemoFusion: Democratising High-Resolution Image Generation With No $$$ | Ruoyi Du, Dongliang Chang, Timothy Hospedales, Yi-Zhe Song, Zhanyu Ma | High-resolution image generation with Generative Artificial Intelligence (GenAI) has immense potential but due to the enormous capital investment required for training it is increasingly centralised to a few large corporations and hidden behind paywalls. This paper aims to democratise high-resolution GenAI by advancing the frontier of high-resolution generation while remaining accessible to a broad audience. We demonstrate that existing Latent Diffusion Models (LDMs) possess untapped potential for higher-resolution image generation. Our novel DemoFusion framework seamlessly extends open-source GenAI models employing Progressive Upscaling Skip Residual and Dilated Sampling mechanisms to achieve higher-resolution image generation. The progressive nature of DemoFusion requires more passes but the intermediate results can serve as "previews" facilitating rapid prompt iteration. | https://openaccess.thecvf.com/content/CVPR2024/papers/Du_DemoFusion_Democratising_High-Resolution_Image_Generation_With_No__CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16973 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Du_DemoFusion_Democratising_High-Resolution_Image_Generation_With_No__CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Du_DemoFusion_Democratising_High-Resolution_Image_Generation_With_No__CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Du_DemoFusion_Democratising_High-Resolution_CVPR_2024_supplemental.pdf | null |
IBD-SLAM: Learning Image-Based Depth Fusion for Generalizable SLAM | Minghao Yin, Shangzhe Wu, Kai Han | In this paper we address the challenging problem of visual SLAM with neural scene representations. Recently neural scene representations have shown promise for SLAM to produce dense 3D scene reconstruction with high quality. However existing methods require scene-specific optimization leading to time-consuming mapping processes for each individual scene. To overcome this limitation we propose IBD-SLAM an Image-Based Depth fusion framework for generalizable SLAM. In particular we adopt a Neural Radiance Field (NeRF) for scene representation. Inspired by multi-view image-based rendering instead of learning a fixed-grid scene representation we propose to learn an image-based depth fusion model that fuses depth maps of multiple reference views into a xyz-map representation. Once trained this model can be applied to new uncalibrated monocular RGBD videos of unseen scenes without the need for retraining and reconstructs full 3D scenes efficiently with a light-weight pose optimization procedure. We thoroughly evaluate IBD-SLAM on public visual SLAM benchmarks outperforming the previous state-of-the-art while being 10x faster in the mapping stage. Project page: https://visual-ai.github.io/ibd-slam. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yin_IBD-SLAM_Learning_Image-Based_Depth_Fusion_for_Generalizable_SLAM_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yin_IBD-SLAM_Learning_Image-Based_Depth_Fusion_for_Generalizable_SLAM_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yin_IBD-SLAM_Learning_Image-Based_Depth_Fusion_for_Generalizable_SLAM_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yin_IBD-SLAM_Learning_Image-Based_CVPR_2024_supplemental.pdf | null |
CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment | Sajid Javed, Arif Mahmood, Iyyakutti Iyappan Ganapathi, Fayaz Ali Dharejo, Naoufel Werghi, Mohammed Bennamoun | This paper proposes Comprehensive Pathology Language Image Pre-training (CPLIP) a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodology enriches vision language models by leveraging extensive data without needing ground truth annotations. CPLIP involves constructing a pathology-specific dictionary generating textual descriptions for images using language models and retrieving relevant images for each text snippet via a pre-trained model. The model is then fine-tuned using a many-to-many contrastive learning method to align complex interrelated concepts across both modalities. Evaluated across multiple histopathology tasks CPLIP shows notable improvements in zero-shot learning scenarios outperforming existing methods in both interpretability and robustness and setting a higher benchmark for the application of vision-language models in the field. To encourage further research and replication the code for CPLIP is available on GitHubat https://cplip.github.io/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Javed_CPLIP_Zero-Shot_Learning_for_Histopathology_with_Comprehensive_Vision-Language_Alignment_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Javed_CPLIP_Zero-Shot_Learning_for_Histopathology_with_Comprehensive_Vision-Language_Alignment_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Javed_CPLIP_Zero-Shot_Learning_for_Histopathology_with_Comprehensive_Vision-Language_Alignment_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Javed_CPLIP_Zero-Shot_Learning_CVPR_2024_supplemental.pdf | null |
Total Selfie: Generating Full-Body Selfies | Bowei Chen, Brian Curless, Ira Kemelmacher-Shlizerman, Steven M. Seitz | We present a method to generate full-body selfies from photographs originally taken at arms length. Because self-captured photos are typically taken close up they have limited field of view and exaggerated perspective that distorts facial shapes. We instead seek to generate the photo some one else would take of you from a few feet away. Our approach takes as input four selfies of your face and body a background image and generates a full-body selfie in a desired target pose. We introduce a novel diffusion-based approach to combine all of this information into high-quality well-composed photos of you with the desired pose and background. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Total_Selfie_Generating_Full-Body_Selfies_CVPR_2024_paper.pdf | http://arxiv.org/abs/2308.14740 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Total_Selfie_Generating_Full-Body_Selfies_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Total_Selfie_Generating_Full-Body_Selfies_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Total_Selfie_Generating_CVPR_2024_supplemental.pdf | null |
Visual Programming for Zero-shot Open-Vocabulary 3D Visual Grounding | Zhihao Yuan, Jinke Ren, Chun-Mei Feng, Hengshuang Zhao, Shuguang Cui, Zhen Li | 3D Visual Grounding (3DVG) aims at localizing 3D object based on textual descriptions. Conventional supervised methods for 3DVG often necessitate extensive annotations and a predefined vocabulary which can be restrictive. To address this issue we propose a novel visual programming approach for zero-shot open-vocabulary 3DVG leveraging the capabilities of large language models (LLMs). Our approach begins with a unique dialog-based method engaging with LLMs to establish a foundational understanding of zero-shot 3DVG. Building on this we design a visual program that consists of three types of modules i.e. view-independent view-dependent and functional modules. Furthermore we develop an innovative language-object correlation module to extend the scope of existing 3D object detectors into open-vocabulary scenarios. Extensive experiments demonstrate that our zero-shot approach can outperform some supervised baselines marking a significant stride towards effective 3DVG. Code is available at https://curryyuan.github.io/ZSVG3D. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yuan_Visual_Programming_for_Zero-shot_Open-Vocabulary_3D_Visual_Grounding_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.15383 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_Visual_Programming_for_Zero-shot_Open-Vocabulary_3D_Visual_Grounding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_Visual_Programming_for_Zero-shot_Open-Vocabulary_3D_Visual_Grounding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yuan_Visual_Programming_for_CVPR_2024_supplemental.pdf | null |
Learning Structure-from-Motion with Graph Attention Networks | Lucas Brynte, José Pedro Iglesias, Carl Olsson, Fredrik Kahl | In this paper we tackle the problem of learning Structure-from-Motion (SfM) through the use of graph attention networks. SfM is a classic computer vision problem that is solved though iterative minimization of reprojection errors referred to as Bundle Adjustment (BA) starting from a good initialization. In order to obtain a good enough initialization to BA conventional methods rely on a sequence of sub-problems (such as pairwise pose estimation pose averaging or triangulation) which provide an initial solution that can then be refined using BA. In this work we replace these sub-problems by learning a model that takes as input the 2D keypoints detected across multiple views and outputs the corresponding camera poses and 3D keypoint coordinates. Our model takes advantage of graph neural networks to learn SfM-specific primitives and we show that it can be used for fast inference of the reconstruction for new and unseen sequences. The experimental results show that the proposed model outperforms competing learning-based methods and challenges COLMAP while having lower runtime. Our code is available at: https://github.com/lucasbrynte/gasfm/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Brynte_Learning_Structure-from-Motion_with_Graph_Attention_Networks_CVPR_2024_paper.pdf | http://arxiv.org/abs/2308.15984 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Brynte_Learning_Structure-from-Motion_with_Graph_Attention_Networks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Brynte_Learning_Structure-from-Motion_with_Graph_Attention_Networks_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Brynte_Learning_Structure-from-Motion_with_CVPR_2024_supplemental.pdf | null |
Geometry Transfer for Stylizing Radiance Fields | Hyunyoung Jung, Seonghyeon Nam, Nikolaos Sarafianos, Sungjoo Yoo, Alexander Sorkine-Hornung, Rakesh Ranjan | Shape and geometric patterns are essential in defining stylistic identity. However current 3D style transfer methods predominantly focus on transferring colors and textures often overlooking geometric aspects. In this paper we introduce Geometry Transfer a novel method that leverages geometric deformation for 3D style transfer. This technique employs depth maps to extract a style guide subsequently applied to stylize the geometry of radiance fields. Moreover we propose new techniques that utilize geometric cues from the 3D scene thereby enhancing aesthetic expressiveness and more accurately reflecting intended styles. Our extensive experiments show that Geometry Transfer enables a broader and more expressive range of stylizations thereby significantly expanding the scope of 3D style transfer. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jung_Geometry_Transfer_for_Stylizing_Radiance_Fields_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.00863 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jung_Geometry_Transfer_for_Stylizing_Radiance_Fields_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jung_Geometry_Transfer_for_Stylizing_Radiance_Fields_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jung_Geometry_Transfer_for_CVPR_2024_supplemental.pdf | null |
Holoported Characters: Real-time Free-viewpoint Rendering of Humans from Sparse RGB Cameras | Ashwath Shetty, Marc Habermann, Guoxing Sun, Diogo Luvizon, Vladislav Golyanik, Christian Theobalt | We present the first approach to render highly realistic free-viewpoint videos of a human actor in general apparel from sparse multi-view recording to display in real-time at an unprecedented 4K resolution. At inference our method only requires four camera views of the moving actor and the respective 3D skeletal pose. It handles actors in wide clothing and reproduces even fine-scale dynamic detail e.g. clothing wrinkles face expressions and hand gestures. At training time our learning-based approach expects dense multi-view video and a rigged static surface scan of the actor. Our method comprises three main stages. Stage 1 is a skeleton-driven neural approach for high-quality capture of the detailed dynamic mesh geometry. Stage 2 is a novel solution to create a view-dependent texture using four test-time camera views as input. Finally stage 3 comprises a new image-based refinement network rendering the final 4K image given the output from the previous stages. Our approach establishes a new benchmark for real-time rendering resolution and quality using sparse input camera views unlocking possibilities for immersive telepresence. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shetty_Holoported_Characters_Real-time_Free-viewpoint_Rendering_of_Humans_from_Sparse_RGB_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.07423 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shetty_Holoported_Characters_Real-time_Free-viewpoint_Rendering_of_Humans_from_Sparse_RGB_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shetty_Holoported_Characters_Real-time_Free-viewpoint_Rendering_of_Humans_from_Sparse_RGB_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shetty_Holoported_Characters_Real-time_CVPR_2024_supplemental.pdf | null |
SEAS: ShapE-Aligned Supervision for Person Re-Identification | Haidong Zhu, Pranav Budhwant, Zhaoheng Zheng, Ram Nevatia | We introduce SEAS using ShapE-Aligned Supervision to enhance appearance-based person re-identification. When recognizing an individual's identity existing methods primarily rely on appearance which can be influenced by the background environment due to a lack of body shape awareness. Although some methods attempt to incorporate other modalities such as gait or body shape they encode the additional modality separately resulting in extra computational costs and lacking an inherent connection with appearance. In this paper we explore the use of implicit 3-D body shape representations as pixel-level guidance to augment the extraction of identity features with body shape knowledge in addition to appearance. Using body shape as supervision rather than as input provides shape-aware enhancements without any increase in computational cost and delivers coherent integration with pixel-wise appearance features. Moreover for video-based person re-identification we align pixel-level features across frames with shape awareness to ensure temporal consistency. Our results demonstrate that incorporating body shape as pixel-level supervision reduces rank-1 errors by 1.4% for frame-based and by 2.5% for video-based re-identification tasks respectively and can also be generalized to other existing appearance-based person re-identification methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_SEAS_ShapE-Aligned_Supervision_for_Person_Re-Identification_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_SEAS_ShapE-Aligned_Supervision_for_Person_Re-Identification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_SEAS_ShapE-Aligned_Supervision_for_Person_Re-Identification_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_SEAS_ShapE-Aligned_Supervision_CVPR_2024_supplemental.zip | null |
Class Incremental Learning with Multi-Teacher Distillation | Haitao Wen, Lili Pan, Yu Dai, Heqian Qiu, Lanxiao Wang, Qingbo Wu, Hongliang Li | Distillation strategies are currently the primary approaches for mitigating forgetting in class incremental learning (CIL). Existing methods generally inherit previous knowledge from a single teacher. However teachers with different mechanisms are talented at different tasks and inheriting diverse knowledge from them can enhance compatibility with new knowledge. In this paper we propose the MTD method to find multiple diverse teachers for CIL. Specifically we adopt weight permutation feature perturbation and diversity regularization techniques to ensure diverse mechanisms in teachers. To reduce time and memory consumption each teacher is represented as a small branch in the model. We adapt existing CIL distillation strategies with MTD and extensive experiments on CIFAR-100 ImageNet-100 and ImageNet-1000 show significant performance improvement. Our code is available at https://github.com/HaitaoWen/CLearning. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wen_Class_Incremental_Learning_with_Multi-Teacher_Distillation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wen_Class_Incremental_Learning_CVPR_2024_supplemental.pdf | null |
Reg-PTQ: Regression-specialized Post-training Quantization for Fully Quantized Object Detector | Yifu Ding, Weilun Feng, Chuyan Chen, Jinyang Guo, Xianglong Liu | Although deep learning based object detection is of great significance for various applications it faces challenges when deployed on edge devices due to the computation and energy limitations. Post-training quantization (PTQ) can improve inference efficiency through integer computing. However they suffer from severe performance degradation when performing full quantization due to overlooking the unique characteristics of regression tasks in object detection. In this paper we are the first to explore regression-friendly quantization and conduct full quantization on various detectors. We reveal the intrinsic reason behind the difficulty of quantizing regressors with empirical and theoretical justifications and introduce a novel Regression-specialized Post-Training Quantization (Reg-PTQ) scheme. It includes Filtered Global Loss Integration Calibration to combine the global loss with a two-step filtering mechanism mitigating the adverse impact of false positive bounding boxes and Learnable Logarithmic-Affine Quantizer tailored for the non-uniform distributed parameters in regression structures. Extensive experiments on prevalent detectors showcase the effectiveness of the well-designed Reg-PTQ. Notably our Reg-PTQ achieves 7.6 times and 5.4 times reduction in computation and storage consumption under INT4 with little performance degradation which indicates the immense potential of fully quantized detectors in real-world object detection applications. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ding_Reg-PTQ_Regression-specialized_Post-training_Quantization_for_Fully_Quantized_Object_Detector_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ding_Reg-PTQ_Regression-specialized_Post-training_Quantization_for_Fully_Quantized_Object_Detector_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ding_Reg-PTQ_Regression-specialized_Post-training_Quantization_for_Fully_Quantized_Object_Detector_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ding_Reg-PTQ_Regression-specialized_Post-training_CVPR_2024_supplemental.pdf | null |
AMU-Tuning: Effective Logit Bias for CLIP-based Few-shot Learning | Yuwei Tang, Zhenyi Lin, Qilong Wang, Pengfei Zhu, Qinghua Hu | Recently pre-trained vision-language models (e.g. CLIP) have shown great potential in few-shot learning and attracted a lot of research interest. Although efforts have been made to improve few-shot ability of CLIP key factors on the effectiveness of existing methods have not been well studied limiting further exploration of CLIP's potential in few-shot learning. In this paper we first introduce a unified formulation to analyze CLIP-based few-shot learning methods from a perspective of logit bias which encourages us to learn an effective logit bias for further improving performance of CLIP-based few-shot learning methods. To this end we disassemble three key components involved in computation of logit bias (i.e. logit features logit predictor and logit fusion) and empirically analyze the effect on performance of few-shot classification. Based on analysis of key components this paper proposes a novel AMU-Tuning method to learn effective logit bias for CLIP-based few-shot classification. Specifically our AMU-Tuning predicts logit bias by exploiting the appropriate Auxiliary features which are fed into an efficient feature-initialized linear classifier with Multi-branch training. Finally an Uncertainty-based fusion is developed to incorporate logit bias into CLIP for few-shot classification. The experiments are conducted on several widely used benchmarks and the results show AMU-Tuning clearly outperforms its counterparts while achieving state-of-the-art performance of CLIP-based few-shot learning without bells and whistles. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tang_AMU-Tuning_Effective_Logit_Bias_for_CLIP-based_Few-shot_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_AMU-Tuning_Effective_Logit_Bias_for_CLIP-based_Few-shot_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_AMU-Tuning_Effective_Logit_Bias_for_CLIP-based_Few-shot_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tang_AMU-Tuning_Effective_Logit_CVPR_2024_supplemental.pdf | null |
Real-World Mobile Image Denoising Dataset with Efficient Baselines | Roman Flepp, Andrey Ignatov, Radu Timofte, Luc Van Gool | The recently increased role of mobile photography has raised the standards of on-device photo processing tremendously. Despite the latest advancements in camera hardware the mobile camera sensor area cannot be increased significantly due to physical constraints leading to a pixel size of 0.6--2.0 \mum which results in strong image noise even in moderate lighting conditions. In the era of deep learning one can train a CNN model to perform robust image denoising. However there is still a lack of a substantially diverse dataset for this task. To address this problem we introduce a novel Mobile Image Denoising Dataset (MIDD) comprising over 400000 noisy / noise-free image pairs captured under various conditions by 20 different mobile camera sensors. Additionally we propose a new DPreview test set consisting of data from 294 different cameras for precise model evaluation. Furthermore we present the efficient baseline model SplitterNet for the considered mobile image denoising task that achieves high numerical and visual results while being able to process 8MP photos directly on smartphone GPUs in under one second. Thereby outperforming models with similar runtimes. This model is also compatible with recent mobile NPUs demonstrating an even higher speed when deployed on them. The conducted experiments demonstrate high robustness of the proposed solution when applied to images from previously unseen sensors showing its high generalizability. The datasets code and models can be found on the official project website. | https://openaccess.thecvf.com/content/CVPR2024/papers/Flepp_Real-World_Mobile_Image_Denoising_Dataset_with_Efficient_Baselines_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Flepp_Real-World_Mobile_Image_Denoising_Dataset_with_Efficient_Baselines_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Flepp_Real-World_Mobile_Image_Denoising_Dataset_with_Efficient_Baselines_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Flepp_Real-World_Mobile_Image_CVPR_2024_supplemental.pdf | null |
Making Vision Transformers Truly Shift-Equivariant | Renan A. Rojas-Gomez, Teck-Yian Lim, Minh N. Do, Raymond A. Yeh | In the field of computer vision Vision Transformers (ViTs) have emerged as a prominent deep learning architecture. Despite being inspired by Convolutional Neural Networks (CNNs) ViTs are susceptible to small spatial shifts in the input data - they lack shift-equivariance. To address this shortcoming we introduce novel data-adaptive designs for each of the ViT modules that break shift-equivariance such as tokenization self-attention patch merging and positional encoding. With our proposed modules we achieve perfect circular shift-equivariance across four prominent ViT architectures: Swin SwinV2 CvT and MViTv2. Additionally we leverage our design to further enhance consistency under standard shifts. We evaluate our adaptive ViT models on image classification and semantic segmentation tasks. Our models achieve competitive performance across three diverse datasets showcasing perfect (100%) circular shift consistency while improving standard shift consistency. | https://openaccess.thecvf.com/content/CVPR2024/papers/Rojas-Gomez_Making_Vision_Transformers_Truly_Shift-Equivariant_CVPR_2024_paper.pdf | http://arxiv.org/abs/2305.16316 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Rojas-Gomez_Making_Vision_Transformers_Truly_Shift-Equivariant_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Rojas-Gomez_Making_Vision_Transformers_Truly_Shift-Equivariant_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Rojas-Gomez_Making_Vision_Transformers_CVPR_2024_supplemental.pdf | null |
SpikeNeRF: Learning Neural Radiance Fields from Continuous Spike Stream | Lin Zhu, Kangmin Jia, Yifan Zhao, Yunshan Qi, Lizhi Wang, Hua Huang | Spike cameras leveraging spike-based integration sampling and high temporal resolution offer distinct advantages over standard cameras. However existing approaches reliant on spike cameras often assume optimal illumination a condition frequently unmet in real-world scenarios. To address this we introduce SpikeNeRF the first work that derives a NeRF-based volumetric scene representation from spike camera data. Our approach leverages NeRF's multi-view consistency to establish robust self-supervision effectively eliminating erroneous measurements and uncovering coherent structures within exceedingly noisy input amidst diverse real-world illumination scenarios. The framework comprises two core elements: a spike generation model incorporating an integrate-and-fire neuron layer and parameters accounting for non-idealities such as threshold variation and a spike rendering loss capable of generalizing across varying illumination conditions. We describe how to effectively optimize neural radiance fields to render photorealistic novel views from the novel continuous spike stream demonstrating advantages over other vision sensors in certain scenes. Empirical evaluations conducted on both real and novel realistically simulated sequences affirm the efficacy of our methodology. The dataset and source code are released at https://github.com/BIT-Vision/SpikeNeRF. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_SpikeNeRF_Learning_Neural_Radiance_Fields_from_Continuous_Spike_Stream_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11222 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_SpikeNeRF_Learning_Neural_Radiance_Fields_from_Continuous_Spike_Stream_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_SpikeNeRF_Learning_Neural_Radiance_Fields_from_Continuous_Spike_Stream_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_SpikeNeRF_Learning_Neural_CVPR_2024_supplemental.pdf | null |
Action Scene Graphs for Long-Form Understanding of Egocentric Videos | Ivan Rodin, Antonino Furnari, Kyle Min, Subarna Tripathi, Giovanni Maria Farinella | We present Egocentric Action Scene Graphs (EASGs) a new representation for long-form understanding of egocentric videos. EASGs extend standard manually-annotated representations of egocentric videos such as verb-noun action labels by providing a temporally evolving graph-based description of the actions performed by the camera wearer including interacted objects their relationships and how actions unfold in time. Through a novel annotation procedure we extend the Ego4D dataset adding manually labeled Egocentric Action Scene Graphs which offer a rich set of annotations for long-from egocentric video understanding. We hence define the EASG generation task and provide a baseline approach establishing preliminary benchmarks. Experiments on two downstream tasks action anticipation and activity summarization highlight the effectiveness of EASGs for long-form egocentric video understanding. We will release the dataset and code to replicate experiments and annotations. | https://openaccess.thecvf.com/content/CVPR2024/papers/Rodin_Action_Scene_Graphs_for_Long-Form_Understanding_of_Egocentric_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.03391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Rodin_Action_Scene_Graphs_for_Long-Form_Understanding_of_Egocentric_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Rodin_Action_Scene_Graphs_for_Long-Form_Understanding_of_Egocentric_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Rodin_Action_Scene_Graphs_CVPR_2024_supplemental.pdf | null |
A Semi-supervised Nighttime Dehazing Baseline with Spatial-Frequency Aware and Realistic Brightness Constraint | Xiaofeng Cong, Jie Gui, Jing Zhang, Junming Hou, Hao Shen | Existing research based on deep learning has extensively explored the problem of daytime image dehazing. However few studies have considered the characteristics of nighttime hazy scenes. There are two distinctions between nighttime and daytime haze. First there may be multiple active colored light sources with lower illumination intensity in nighttime scenes which may cause haze glow and noise with localized coupled and frequency inconsistent characteristics. Second due to the domain discrepancy between simulated and real-world data unrealistic brightness may occur when applying a dehazing model trained on simulated data to real-world data. To address the above two issues we propose a semi-supervised model for real-world nighttime dehazing. First the spatial attention and frequency spectrum filtering are implemented as a spatial-frequency domain information interaction module to handle the first issue. Second a pseudo-label-based retraining strategy and a local window-based brightness loss for semi-supervised training process is designed to suppress haze and glow while achieving realistic brightness. Experiments on public benchmarks validate the effectiveness of the proposed method and its superiority over state-of-the-art methods. The source code and Supplementary Materials are placed in the https://github.com/Xiaofeng-life/SFSNiD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cong_A_Semi-supervised_Nighttime_Dehazing_Baseline_with_Spatial-Frequency_Aware_and_Realistic_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18548 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cong_A_Semi-supervised_Nighttime_Dehazing_Baseline_with_Spatial-Frequency_Aware_and_Realistic_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cong_A_Semi-supervised_Nighttime_Dehazing_Baseline_with_Spatial-Frequency_Aware_and_Realistic_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cong_A_Semi-supervised_Nighttime_CVPR_2024_supplemental.pdf | null |
De-confounded Data-free Knowledge Distillation for Handling Distribution Shifts | Yuzheng Wang, Dingkang Yang, Zhaoyu Chen, Yang Liu, Siao Liu, Wenqiang Zhang, Lihua Zhang, Lizhe Qi | Data-Free Knowledge Distillation (DFKD) is a promising task to train high-performance small models to enhance actual deployment without relying on the original training data. Existing methods commonly avoid relying on private data by utilizing synthetic or sampled data. However a long-overlooked issue is that the severe distribution shifts between their substitution and original data which manifests as huge differences in the quality of images and class proportions. The harmful shifts are essentially the confounder that significantly causes performance bottlenecks. To tackle the issue this paper proposes a novel perspective with causal inference to disentangle the student models from the impact of such shifts. By designing a customized causal graph we first reveal the causalities among the variables in the DFKD task. Subsequently we propose a Knowledge Distillation Causal Intervention (KDCI) framework based on the backdoor adjustment to de-confound the confounder. KDCI can be flexibly combined with most existing state-of-the-art baselines. Experiments in combination with six representative DFKD methods demonstrate the effectiveness of our KDCI which can obviously help existing methods under almost all settings e.g. improving the baseline by up to 15.54% accuracy on the CIFAR-100 dataset. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_De-confounded_Data-free_Knowledge_Distillation_for_Handling_Distribution_Shifts_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19539 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_De-confounded_Data-free_Knowledge_Distillation_for_Handling_Distribution_Shifts_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_De-confounded_Data-free_Knowledge_Distillation_for_Handling_Distribution_Shifts_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_De-confounded_Data-free_Knowledge_CVPR_2024_supplemental.pdf | null |
Fine-Grained Bipartite Concept Factorization for Clustering | Chong Peng, Pengfei Zhang, Yongyong Chen, Zhao Kang, Chenglizhao Chen, Qiang Cheng | In this paper we propose a novel concept factorization method that seeks factor matrices using a cross-order positive semi-definite neighbor graph which provides comprehensive and complementary neighbor information of the data. The factor matrices are learned with bipartite graph partitioning which exploits explicit cluster structure of the data and is more geared towards clustering application. We develop an effective and efficient optimization algorithm for our method and provide elegant theoretical results about the convergence. Extensive experimental results confirm the effectiveness of the proposed method. | https://openaccess.thecvf.com/content/CVPR2024/papers/Peng_Fine-Grained_Bipartite_Concept_Factorization_for_Clustering_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_Fine-Grained_Bipartite_Concept_Factorization_for_Clustering_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_Fine-Grained_Bipartite_Concept_Factorization_for_Clustering_CVPR_2024_paper.html | CVPR 2024 | null | null |
Siamese Learning with Joint Alignment and Regression for Weakly-Supervised Video Paragraph Grounding | Chaolei Tan, Jianhuang Lai, Wei-Shi Zheng, Jian-Fang Hu | Video Paragraph Grounding (VPG) is an emerging task in video-language understanding which aims at localizing multiple sentences with semantic relations and temporal order from an untrimmed video. However existing VPG approaches are heavily reliant on a considerable number of temporal labels that are laborious and time-consuming to acquire. In this work we introduce and explore Weakly-Supervised Video Paragraph Grounding (WSVPG) to eliminate the need of temporal annotations. Different from previous weakly-supervised grounding frameworks based on multiple instance learning or reconstruction learning for two-stage candidate ranking we propose a novel siamese learning framework that jointly learns the cross-modal feature alignment and temporal coordinate regression without timestamp labels to achieve concise one-stage localization for WSVPG. Specifically we devise a Siamese Grounding TRansformer (SiamGTR) consisting of two weight-sharing branches for learning complementary supervision. An Augmentation Branch is utilized for directly regressing the temporal boundaries of a complete paragraph within a pseudo video and an Inference Branch is designed to capture the order-guided feature correspondence for localizing multiple sentences in a normal video. We demonstrate by extensive experiments that our paradigm has superior practicability and flexibility to achieve efficient weakly-supervised or semi-supervised learning outperforming state-of-the-art methods trained with the same or stronger supervision. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tan_Siamese_Learning_with_Joint_Alignment_and_Regression_for_Weakly-Supervised_Video_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Siamese_Learning_with_Joint_Alignment_and_Regression_for_Weakly-Supervised_Video_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Siamese_Learning_with_Joint_Alignment_and_Regression_for_Weakly-Supervised_Video_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tan_Siamese_Learning_with_CVPR_2024_supplemental.pdf | null |
Language-Driven Anchors for Zero-Shot Adversarial Robustness | Xiao Li, Wei Zhang, Yining Liu, Zhanhao Hu, Bo Zhang, Xiaolin Hu | Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks. Previous researches mainly focus on improving adversarial robustness in the fully supervised setting leaving the challenging domain of zero-shot adversarial robustness an open question. In this work we investigate this domain by leveraging the recent advances in large vision-language models such as CLIP to introduce zero-shot adversarial robustness to DNNs. We propose LAAT a Language-driven Anchor-based Adversarial Training strategy. LAAT utilizes the features of a text encoder for each category as fixed anchors (normalized feature embeddings) for each category which are then employed for adversarial training. By leveraging the semantic consistency of the text encoders LAAT aims to enhance the adversarial robustness of the image model on novel categories. However naively using text encoders leads to poor results. Through analysis we identified the issue to be the high cosine similarity between text encoders. We then design an expansion algorithm and an alignment cross-entropy loss to alleviate the problem. Our experimental results demonstrated that LAAT significantly improves zero-shot adversarial robustness over state-of-the-art methods. LAAT has the potential to enhance adversarial robustness by large-scale multimodal models especially when labeled data is unavailable during training. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Language-Driven_Anchors_for_Zero-Shot_Adversarial_Robustness_CVPR_2024_paper.pdf | http://arxiv.org/abs/2301.13096 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Language-Driven_Anchors_for_Zero-Shot_Adversarial_Robustness_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Language-Driven_Anchors_for_Zero-Shot_Adversarial_Robustness_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Language-Driven_Anchors_for_CVPR_2024_supplemental.pdf | null |
Deep Equilibrium Diffusion Restoration with Parallel Sampling | Jiezhang Cao, Yue Shi, Kai Zhang, Yulun Zhang, Radu Timofte, Luc Van Gool | Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images achieving promising performance. Due to the inherent property of diffusion models most existing methods need long serial sampling chains to restore HQ images step-by-step resulting in expensive sampling time and high computation costs. Moreover such long sampling chains hinder understanding the relationship between inputs and restoration results since it is hard to compute the gradients in the whole chains. In this work we aim to rethink the diffusion model-based IR models through a different perspective i.e. a deep equilibrium (DEQ) fixed point system called DeqIR. Specifically we derive an analytical solution by modeling the entire sampling chain in these IR models as a joint multivariate fixed point system. Based on the analytical solution we can conduct parallel sampling and restore HQ images without training. Furthermore we compute fast gradients via DEQ inversion and found that initialization optimization can boost image quality and control the generation direction. Extensive experiments on benchmarks demonstrate the effectiveness of our method on typical IR tasks and real-world settings. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cao_Deep_Equilibrium_Diffusion_Restoration_with_Parallel_Sampling_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.11600 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_Deep_Equilibrium_Diffusion_Restoration_with_Parallel_Sampling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_Deep_Equilibrium_Diffusion_Restoration_with_Parallel_Sampling_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cao_Deep_Equilibrium_Diffusion_CVPR_2024_supplemental.pdf | null |
LEOD: Label-Efficient Object Detection for Event Cameras | Ziyi Wu, Mathias Gehrig, Qing Lyu, Xudong Liu, Igor Gilitschenski | Object detection with event cameras benefits from the sensor's low latency and high dynamic range. However it is costly to fully label event streams for supervised training due to their high temporal resolution. To reduce this cost we present LEOD the first method for label-efficient event-based detection. Our approach unifies weakly- and semi-supervised object detection with a self-training mechanism. We first utilize a detector pre-trained on limited labels to produce pseudo ground truth on unlabeled events. Then the detector is re-trained with both real and generated labels. Leveraging the temporal consistency of events we run bi-directional inference and apply tracking-based post-processing to enhance the quality of pseudo labels. To stabilize training against label noise we further design a soft anchor assignment strategy. We introduce new experimental protocols to evaluate the task of label-efficient event-based detection on Gen1 and 1Mpx datasets. LEOD consistently outperforms supervised baselines across various labeling ratios. For example on Gen1 it improves mAP by 8.6% and 7.8% for RVT-S trained with 1% and 2% labels. On 1Mpx RVT-S with 10% labels even surpasses its fully-supervised counterpart using 100% labels. LEOD maintains its effectiveness even when all labeled data are available reaching new state-of-the-art results. Finally we show that our method readily scales to improve larger detectors as well. Code is released at https://github.com/Wuziyi616/LEOD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_LEOD_Label-Efficient_Object_Detection_for_Event_Cameras_CVPR_2024_paper.pdf | https://arxiv.org/abs/2311.17286 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_LEOD_Label-Efficient_Object_Detection_for_Event_Cameras_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_LEOD_Label-Efficient_Object_Detection_for_Event_Cameras_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_LEOD_Label-Efficient_Object_CVPR_2024_supplemental.pdf | https://openaccess.thecvf.com |
Morphological Prototyping for Unsupervised Slide Representation Learning in Computational Pathology | Andrew H. Song, Richard J. Chen, Tong Ding, Drew F.K. Williamson, Guillaume Jaume, Faisal Mahmood | Representation learning of pathology whole-slide images (WSIs) has been has primarily relied on weak supervision with Multiple Instance Learning (MIL). However the slide representations resulting from this approach are highly tailored to specific clinical tasks which limits their expressivity and generalization particularly in scenarios with limited data. Instead we hypothesize that morphological redundancy in tissue can be leveraged to build a task-agnostic slide representation in an unsupervised fashion. To this end we introduce PANTHER a prototype-based approach rooted in the Gaussian mixture model that summarizes the set of WSI patches into a much smaller set of morphological prototypes. Specifically each patch is assumed to have been generated from a mixture distribution where each mixture component represents a morphological exemplar. Utilizing the estimated mixture parameters we then construct a compact slide representation that can be readily used for a wide range of downstream tasks. By performing an extensive evaluation of PANTHER on subtyping and survival tasks using 13 datasets we show that 1) PANTHER outperforms or is on par with supervised MIL baselines and 2) the analysis of morphological prototypes brings new qualitative and quantitative insights into model interpretability. The code is available at https://github.com/mahmoodlab/Panther. | https://openaccess.thecvf.com/content/CVPR2024/papers/Song_Morphological_Prototyping_for_Unsupervised_Slide_Representation_Learning_in_Computational_Pathology_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.11643 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Song_Morphological_Prototyping_for_Unsupervised_Slide_Representation_Learning_in_Computational_Pathology_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Song_Morphological_Prototyping_for_Unsupervised_Slide_Representation_Learning_in_Computational_Pathology_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Song_Morphological_Prototyping_for_CVPR_2024_supplemental.pdf | null |
Fooling Polarization-Based Vision using Locally Controllable Polarizing Projection | Zhuoxiao Li, Zhihang Zhong, Shohei Nobuhara, Ko Nishino, Yinqiang Zheng | Polarization is a fundamental property of light that encodes abundant information regarding surface shape material illumination and viewing geometry. The computer vision community has witnessed a blossom of polarization-based vision applications such as reflection removal shape-from-polarization (SfP) transparent object segmentation and color constancy partially due to the emergence of single-chip mono/color polarization sensors that make polarization data acquisition easier than ever. However is polarization-based vision vulnerable to adversarial attacks? If so is that possible to realize these adversarial attacks in the physical world without being perceived by human eyes? In this paper we warn the community of the vulnerability of polarization-based vision which can be more serious than RGB-based vision. By adapting a commercial LCD projector we achieve locally controllable polarizing projection which is successfully utilized to fool state-of-the-art polarization-based vision algorithms for glass segmentation and SfP. Compared with existing physical attacks on RGB-based vision which always suffer from the trade-off between attack efficacy and eye conceivability the adversarial attackers based on polarizing projection are contact-free and visually imperceptible since naked human eyes can rarely perceive the difference of viciously manipulated polarizing light and ordinary illumination. This poses unprecedented risks on polarization-based vision for which due attentions should be paid and counter measures be considered. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Fooling_Polarization-Based_Vision_using_Locally_Controllable_Polarizing_Projection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2303.17890 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Fooling_Polarization-Based_Vision_using_Locally_Controllable_Polarizing_Projection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Fooling_Polarization-Based_Vision_using_Locally_Controllable_Polarizing_Projection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Fooling_Polarization-Based_Vision_CVPR_2024_supplemental.pdf | null |
Dense Optical Tracking: Connecting the Dots | Guillaume Le Moing, Jean Ponce, Cordelia Schmid | Recent approaches to point tracking are able to recover the trajectory of any scene point through a large portion of a video despite the presence of occlusions. They are however too slow in practice to track every point observed in a single frame in a reasonable amount of time. This paper introduces DOT a novel simple and efficient method for solving this problem. It first extracts a small set of tracks from key regions at motion boundaries using an off-the-shelf point tracking algorithm. Given source and target frames DOT then computes rough initial estimates of a dense flow field and visibility mask through nearest-neighbor interpolation before refining them using a learnable optical flow estimator that explicitly handles occlusions and can be trained on synthetic data with ground-truth correspondences. We show that DOT is significantly more accurate than current optical flow techniques outperforms sophisticated "universal" trackers like OmniMotion and is on par with or better than the best point tracking algorithms like CoTracker while being at least two orders of magnitude faster. Quantitative and qualitative experiments with synthetic and real videos validate the promise of the proposed approach. Code data and videos showcasing the capabilities of our approach are available in the project webpage: https://16lemoing.github.io/dot . | https://openaccess.thecvf.com/content/CVPR2024/papers/Le_Moing_Dense_Optical_Tracking_Connecting_the_Dots_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.00786 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Le_Moing_Dense_Optical_Tracking_Connecting_the_Dots_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Le_Moing_Dense_Optical_Tracking_Connecting_the_Dots_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Le_Moing_Dense_Optical_Tracking_CVPR_2024_supplemental.pdf | null |
A Stealthy Wrongdoer: Feature-Oriented Reconstruction Attack against Split Learning | Xiaoyang Xu, Mengda Yang, Wenzhe Yi, Ziang Li, Juan Wang, Hongxin Hu, Yong Zhuang, Yaxin Liu | Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces a new semi-honest Data Reconstruction Attack on SL named Feature-Oriented Reconstruction Attack (FORA). In contrast to prior works FORA relies on limited prior knowledge specifically that the server utilizes auxiliary samples from the public without knowing any client's private information. This allows FORA to conduct the attack stealthily and achieve robust performance. The key vulnerability exploited by FORA is the revelation of the model representation preference in the smashed data output by victim client. FORA constructs a substitute client through feature-level transfer learning aiming to closely mimic the victim client's representation preference. Leveraging this substitute client the server trains the attack model to effectively reconstruct private data. Extensive experiments showcase FORA's superior performance compared to state-of-the-art methods. Furthermore the paper systematically evaluates the proposed method's applicability across diverse settings and advanced defense strategies. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_A_Stealthy_Wrongdoer_Feature-Oriented_Reconstruction_Attack_against_Split_Learning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.04115 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_A_Stealthy_Wrongdoer_Feature-Oriented_Reconstruction_Attack_against_Split_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_A_Stealthy_Wrongdoer_Feature-Oriented_Reconstruction_Attack_against_Split_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_A_Stealthy_Wrongdoer_CVPR_2024_supplemental.pdf | null |
DiffAM: Diffusion-based Adversarial Makeup Transfer for Facial Privacy Protection | Yuhao Sun, Lingyun Yu, Hongtao Xie, Jiaming Li, Yongdong Zhang | With the rapid development of face recognition (FR) systems the privacy of face images on social media is facing severe challenges due to the abuse of unauthorized FR systems. Some studies utilize adversarial attack techniques to defend against malicious FR systems by generating adversarial examples. However the generated adversarial examples i.e. the protected face images tend to suffer from subpar visual quality and low transferability. In this paper we propose a novel face protection approach dubbed DiffAM which leverages the powerful generative ability of diffusion models to generate high-quality protected face images with adversarial makeup transferred from reference images. To be specific we first introduce a makeup removal module to generate non-makeup images utilizing a fine-tuned diffusion model with guidance of textual prompts in CLIP space. As the inverse process of makeup transfer makeup removal can make it easier to establish the deterministic relationship between makeup domain and non-makeup domain regardless of elaborate text prompts. Then with this relationship a CLIP-based makeup loss along with an ensemble attack strategy is introduced to jointly guide the direction of adversarial makeup domain achieving the generation of protected face images with natural-looking makeup and high black-box transferability. Extensive experiments demonstrate that DiffAM achieves higher visual quality and attack success rates with a gain of 12.98% under black-box setting compared with the state of the arts. The code will be available at https://github.com/HansSunY/DiffAM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_DiffAM_Diffusion-based_Adversarial_Makeup_Transfer_for_Facial_Privacy_Protection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.09882 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_DiffAM_Diffusion-based_Adversarial_Makeup_Transfer_for_Facial_Privacy_Protection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_DiffAM_Diffusion-based_Adversarial_Makeup_Transfer_for_Facial_Privacy_Protection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_DiffAM_Diffusion-based_Adversarial_CVPR_2024_supplemental.pdf | null |
SlowFormer: Adversarial Attack on Compute and Energy Consumption of Efficient Vision Transformers | K L Navaneet, Soroush Abbasi Koohpayegani, Essam Sleiman, Hamed Pirsiavash | Recently there has been a lot of progress in reducing the computation of deep models at inference time. These methods can reduce both the computational needs and power usage of deep models. Some of these approaches adaptively scale the compute based on the input instance. We show that such models can be vulnerable to a universal adversarial patch attack where the attacker optimizes for a patch that when pasted on any image can increase the compute and power consumption of the model. We run experiments with three different efficient vision transformer methods showing that in some cases the attacker can increase the computation to the maximum possible level by simply pasting a patch that occupies only 8% of the image area. We also show that a standard adversarial training defense method can reduce some of the attack's success. We believe adaptive efficient methods will be necessary for the future to lower the power usage of expensive deep models so we hope our paper encourages the community to study the robustness of these methods and develop better defense methods for the proposed attack. Code is available at: https://github.com/UCDvision/SlowFormer. | https://openaccess.thecvf.com/content/CVPR2024/papers/Navaneet_SlowFormer_Adversarial_Attack_on_Compute_and_Energy_Consumption_of_Efficient_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Navaneet_SlowFormer_Adversarial_Attack_on_Compute_and_Energy_Consumption_of_Efficient_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Navaneet_SlowFormer_Adversarial_Attack_on_Compute_and_Energy_Consumption_of_Efficient_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Navaneet_SlowFormer_Adversarial_Attack_CVPR_2024_supplemental.pdf | null |
TULIP: Transformer for Upsampling of LiDAR Point Clouds | Bin Yang, Patrick Pfreundschuh, Roland Siegwart, Marco Hutter, Peyman Moghadam, Vaishakh Patil | LiDAR Upsampling is a challenging task for the perception systems of robots and autonomous vehicles due to the sparse and irregular structure of large-scale scene contexts. Recent works propose to solve this problem by converting LiDAR data from 3D Euclidean space into an image super-resolution problem in 2D image space. Although their methods can generate high-resolution range images with fine-grained details the resulting 3D point clouds often blur out details and predict invalid points. In this paper we propose TULIP a new method to reconstruct high-resolution LiDAR point clouds from low-resolution LiDAR input. We also follow a range image-based approach but specifically modify the patch and window geometries of a Swin-Transformer-based network to better fit the characteristics of range images. We conducted several experiments on three public real-world and simulated datasets. TULIP outperforms state-of-the-art methods in all relevant metrics and generates robust and more realistic point clouds than prior works. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_TULIP_Transformer_for_Upsampling_of_LiDAR_Point_Clouds_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.06733 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_TULIP_Transformer_for_Upsampling_of_LiDAR_Point_Clouds_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_TULIP_Transformer_for_Upsampling_of_LiDAR_Point_Clouds_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_TULIP_Transformer_for_CVPR_2024_supplemental.pdf | null |
How to Configure Good In-Context Sequence for Visual Question Answering | Li Li, Jiawei Peng, Huiyi Chen, Chongyang Gao, Xu Yang | Inspired by the success of Large Language Models in dealing with new tasks via In-Context Learning (ICL) in NLP researchers have also developed Large Vision-Language Models (LVLMs) with ICL capabilities. However when implementing ICL using these LVLMs researchers usually resort to the simplest way like random sampling to configure the in-context sequence thus leading to sub-optimal results. To enhance the ICL performance in this study we use Visual Question Answering (VQA) as case study to explore diverse in-context configurations to find the powerful ones. Additionally through observing the changes of the LVLM outputs by altering the in-context sequence we gain insights into the inner properties of LVLMs improving our understanding of them. Specifically to explore in-context configurations we design diverse retrieval methods and employ different strategies to manipulate the retrieved demonstrations. Through exhaustive experiments on three VQA datasets: VQAv2 VizWiz and OK-VQA we uncover three important inner properties of the applied LVLM and demonstrate which strategies can consistently improve the ICL VQA performance. Our code is provided in: https: //github.com/GaryJiajia/OFv2_ICL_VQA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_How_to_Configure_Good_In-Context_Sequence_for_Visual_Question_Answering_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.01571 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_How_to_Configure_Good_In-Context_Sequence_for_Visual_Question_Answering_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_How_to_Configure_Good_In-Context_Sequence_for_Visual_Question_Answering_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_How_to_Configure_CVPR_2024_supplemental.pdf | null |
Gaussian Shell Maps for Efficient 3D Human Generation | Rameen Abdal, Wang Yifan, Zifan Shi, Yinghao Xu, Ryan Po, Zhengfei Kuang, Qifeng Chen, Dit-Yan Yeung, Gordon Wetzstein | Efficient generation of 3D digital humans is important in several industries including virtual reality social media and cinematic production. 3D generative adversarial networks (GANs) have demonstrated state-of-the-art (SOTA) quality and diversity for generated assets. Current 3D GAN architectures however typically rely on volume representations which are slow to render thereby hampering the GAN training and requiring multi-view-inconsistent 2D upsamplers. Here we introduce Gaussian Shell Maps (GSMs) as a framework that connects SOTA generator network architectures with emerging 3D Gaussian rendering primitives using an articulable multi shell--based scaffold. In this setting a CNN generates a 3D texture stack with features that are mapped to the shells. The latter represent inflated and deflated versions of a template surface of a digital human in a canonical body pose. Instead of rasterizing the shells directly we sample 3D Gaussians on the shells whose attributes are encoded in the texture features. These Gaussians are efficiently and differentiably rendered. The ability to articulate the shells is important during GAN training and at inference time to deform a body into arbitrary user-defined poses. Our efficient rendering scheme bypasses the need for view-inconsistent upsamplers and achieves high-quality multi-view consistent renderings at a native resolution of 512 x512 pixels. We demonstrate that GSMs successfully generate 3D humans when trained on single-view datasets including SHHQ and DeepFashion. | https://openaccess.thecvf.com/content/CVPR2024/papers/Abdal_Gaussian_Shell_Maps_for_Efficient_3D_Human_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17857 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Abdal_Gaussian_Shell_Maps_for_Efficient_3D_Human_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Abdal_Gaussian_Shell_Maps_for_Efficient_3D_Human_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Abdal_Gaussian_Shell_Maps_CVPR_2024_supplemental.pdf | null |
Defense Against Adversarial Attacks on No-Reference Image Quality Models with Gradient Norm Regularization | Yujia Liu, Chenxi Yang, Dingquan Li, Jianhao Ding, Tingting Jiang | The task of No-Reference Image Quality Assessment (NR-IQA) is to estimate the quality score of an input image without additional information. NR-IQA models play a crucial role in the media industry aiding in performance evaluation and optimization guidance. However these models are found to be vulnerable to adversarial attacks which introduce imperceptible perturbations to input images resulting in significant changes in predicted scores. In this paper we propose a defense method to mitigate the variability in predicted scores caused by small perturbations thus enhancing the adversarial robustness of NR-IQA models. To be specific we present theoretical evidence showing that the extent of score changes is related to the l_1 norm of the gradient of the predicted score with respect to the input image when adversarial perturbations are l_inf-bounded. Building on this theoretical foundation we propose a norm regularization training strategy aimed at reducing the l_1 norm of the gradient thereby boosting the adversarial robustness of NR-IQA models. Experiments conducted on four NR-IQA baseline models demonstrate the effectiveness of our strategy in reducing score changes in the presence of adversarial attacks. To the best of our knowledge this work marks the first attempt to defend against adversarial attacks on NR-IQA models. Our study offers valuable insights into the adversarial robustness of NR-IQA models and provides a foundation for future research in this area. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Defense_Against_Adversarial_Attacks_on_No-Reference_Image_Quality_Models_with_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11397 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Defense_Against_Adversarial_Attacks_on_No-Reference_Image_Quality_Models_with_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Defense_Against_Adversarial_Attacks_on_No-Reference_Image_Quality_Models_with_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Defense_Against_Adversarial_CVPR_2024_supplemental.pdf | null |
TACO: Benchmarking Generalizable Bimanual Tool-ACtion-Object Understanding | Yun Liu, Haolin Yang, Xu Si, Ling Liu, Zipeng Li, Yuxiang Zhang, Yebin Liu, Li Yi | Humans commonly work with multiple objects in daily life and can intuitively transfer manipulation skills to novel objects by understanding object functional regularities. However existing technical approaches for analyzing and synthesizing hand-object manipulation are mostly limited to handling a single hand and object due to the lack of data support. To address this we construct TACO an extensive bimanual hand-object-interaction dataset spanning a large variety of tool-action-object compositions for daily human activities. TACO contains 2.5K motion sequences paired with third-person and egocentric views precise hand-object 3D meshes and action labels. To rapidly expand the data scale we present a fully automatic data acquisition pipeline combining multi-view sensing with an optical motion capture system. With the vast research fields provided by TACO we benchmark three generalizable hand-object-interaction tasks: compositional action recognition generalizable hand-object motion forecasting and cooperative grasp synthesis. Extensive experiments reveal new insights challenges and opportunities for advancing the studies of generalizable hand-object motion analysis and synthesis. Our data and code are available at https://taco2024.github.io. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_TACO_Benchmarking_Generalizable_Bimanual_Tool-ACtion-Object_Understanding_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.08399 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_TACO_Benchmarking_Generalizable_Bimanual_Tool-ACtion-Object_Understanding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_TACO_Benchmarking_Generalizable_Bimanual_Tool-ACtion-Object_Understanding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_TACO_Benchmarking_Generalizable_CVPR_2024_supplemental.pdf | null |
MoST: Motion Style Transformer Between Diverse Action Contents | Boeun Kim, Jungho Kim, Hyung Jin Chang, Jin Young Choi | While existing motion style transfer methods are effective between two motions with identical content their performance significantly diminishes when transferring style between motions with different contents. This challenge lies in the lack of clear separation between content and style of a motion. To tackle this challenge we propose a novel motion style transformer that effectively disentangles style from content and generates a plausible motion with transferred style from a source motion. Our distinctive approach to achieving the goal of disentanglement is twofold: (1) a new architecture for motion style transformer with 'part-attentive style modulator across body parts' and 'Siamese encoders that encode style and content features separately'; (2) style disentanglement loss. Our method outperforms existing methods and demonstrates exceptionally high quality particularly in motion pairs with different contents without the need for heuristic post-processing. Codes are available at https://github.com/Boeun-Kim/MoST. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_MoST_Motion_Style_Transformer_Between_Diverse_Action_Contents_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.06225 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_MoST_Motion_Style_Transformer_Between_Diverse_Action_Contents_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_MoST_Motion_Style_Transformer_Between_Diverse_Action_Contents_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_MoST_Motion_Style_CVPR_2024_supplemental.zip | null |
Prompting Hard or Hardly Prompting: Prompt Inversion for Text-to-Image Diffusion Models | Shweta Mahajan, Tanzila Rahman, Kwang Moo Yi, Leonid Sigal | The quality of the prompts provided to text-to-image diffusion models determines how faithful the generated content is to the user's intent often requiring `prompt engineering'. To harness visual concepts from target images without prompt engineering current approaches largely rely on embedding inversion by optimizing and then mapping them to pseudo-tokens. However working with such high-dimensional vector representations is challenging because they lack semantics and interpretability and only allow simple vector operations when using them. Instead this work focuses on inverting the diffusion model to obtain interpretable language prompts directly. The challenge of doing this lies in the fact that the resulting optimization problem is fundamentally discrete and the space of prompts is exponentially large; this makes using standard optimization techniques such as stochastic gradient descent difficult. To this end we utilize a delayed projection scheme to optimize for prompts representative of the vocabulary space in the model. Further we leverage the findings that different timesteps of the diffusion process cater to different levels of detail in an image. The later noisy timesteps of the forward diffusion process correspond to the semantic information and therefore prompt inversion in this range provides tokens representative of the image semantics. We show that our approach can identify semantically interpretable and meaningful prompts for a target image which can be used to synthesize diverse images with similar content. We further illustrate the application of the optimized prompts in evolutionary image generation and concept removal. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mahajan_Prompting_Hard_or_Hardly_Prompting_Prompt_Inversion_for_Text-to-Image_Diffusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.12416 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mahajan_Prompting_Hard_or_Hardly_Prompting_Prompt_Inversion_for_Text-to-Image_Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mahajan_Prompting_Hard_or_Hardly_Prompting_Prompt_Inversion_for_Text-to-Image_Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mahajan_Prompting_Hard_or_CVPR_2024_supplemental.pdf | null |
Unmixing Before Fusion: A Generalized Paradigm for Multi-Source-based Hyperspectral Image Synthesis | Yang Yu, Erting Pan, Xinya Wang, Yuheng Wu, Xiaoguang Mei, Jiayi Ma | In the realm of AI data serves as a pivotal resource. Real-world hyperspectral images (HSIs) bearing wide spectral characteristics are particularly valuable. However the acquisition of HSIs is always costly and time-intensive resulting in a severe data-thirsty issue in HSI research and applications. Current solutions have not been able to generate a sufficient volume of diverse and reliable synthetic HSIs. To this end our study formulates a novel generalized paradigm for HSI synthesis i.e. unmixing before fusion that initiates with unmixing across multi-source data and follows by fusion-based synthesis. By integrating unmixing this work maps unpaired HSI and RGB data to a low-dimensional abundance space greatly alleviating the difficulty of generating high-dimensional samples. Moreover incorporating abundances inferred from unpaired RGB images into generative models allows for cost-effective supplementation of various realistic spatial distributions in abundance synthesis. Our proposed paradigm can be instrumental with a series of deep generative models filling a significant gap in the field and enabling the generation of vast high-quality HSI samples for large-scale downstream tasks. Extension experiments on downstream tasks demonstrate the effectiveness of synthesized HSIs. The code is available at: HSI-Synthesis.github.io. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Unmixing_Before_Fusion_A_Generalized_Paradigm_for_Multi-Source-based_Hyperspectral_Image_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Unmixing_Before_Fusion_A_Generalized_Paradigm_for_Multi-Source-based_Hyperspectral_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Unmixing_Before_Fusion_A_Generalized_Paradigm_for_Multi-Source-based_Hyperspectral_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_Unmixing_Before_Fusion_CVPR_2024_supplemental.pdf | null |
AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis | Tang Tao, Guangrun Wang, Yixing Lao, Peng Chen, Jie Liu, Liang Lin, Kaicheng Yu, Xiaodan Liang | Neural implicit fields have been a de facto standard in novel view synthesis. Recently there exist some methods exploring fusing multiple modalities within a single field aiming to share implicit features from different modalities to enhance reconstruction performance. However these modalities often exhibit misaligned behaviors: optimizing for one modality such as LiDAR can adversely affect another like camera performance and vice versa. In this work we conduct comprehensive analyses on the multimodal implicit field of LiDAR-camera joint synthesis revealing the underlying issue lies in the misalignment of different sensors. Furthermore we introduce AlignMiF a geometrically aligned multimodal implicit field with two proposed modules: Geometry-Aware Alignment (GAA) and Shared Geometry Initialization (SGI). These modules effectively align the coarse geometry across different modalities significantly enhancing the fusion process between LiDAR and camera data. Through extensive experiments across various datasets and scenes we demonstrate the effectiveness of our approach in facilitating better interaction between LiDAR and camera modalities within a unified neural field. Specifically our proposed AlignMiF achieves remarkable improvement over recent implicit fusion methods (+2.01 and +3.11 image PSNR on the KITTI-360 and Waymo datasets) and consistently surpasses single modality performance (13.8% and 14.2% reduction in LiDAR Chamfer Distance on the respective datasets). | https://openaccess.thecvf.com/content/CVPR2024/papers/Tao_AlignMiF_Geometry-Aligned_Multimodal_Implicit_Field_for_LiDAR-Camera_Joint_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17483 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tao_AlignMiF_Geometry-Aligned_Multimodal_Implicit_Field_for_LiDAR-Camera_Joint_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tao_AlignMiF_Geometry-Aligned_Multimodal_Implicit_Field_for_LiDAR-Camera_Joint_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tao_AlignMiF_Geometry-Aligned_Multimodal_CVPR_2024_supplemental.pdf | null |
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation | Kangfu Mei, Mauricio Delbracio, Hossein Talebi, Zhengzhong Tu, Vishal M. Patel, Peyman Milanfar | Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks such as image enhancement restoration editing and compositing. However their widespread adoption is hindered by the high computational cost which limits their real-time application. To address this challenge we introduce a novel method dubbed CoDi that adapts a pre-trained latent diffusion model to accept additional image conditioning inputs while significantly reducing the sampling steps required to achieve high-quality results. Our method can leverage architectures such as ControlNet to incorporate conditioning inputs without compromising the model's prior knowledge gained during large scale pre-training. Additionally a conditional consistency loss enforces consistent predictions across diffusion steps effectively compelling the model to generate high-quality images with conditions in a few steps. Our conditional-task learning and distillation approach outperforms previous distillation methods achieving a new state-of-the-art in producing high-quality images with very few steps (e.g. 1-4) across multiple tasks including super-resolution text-guided image editing and depth-to-image generation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mei_CoDi_Conditional_Diffusion_Distillation_for_Higher-Fidelity_and_Faster_Image_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.01407 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mei_CoDi_Conditional_Diffusion_Distillation_for_Higher-Fidelity_and_Faster_Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mei_CoDi_Conditional_Diffusion_Distillation_for_Higher-Fidelity_and_Faster_Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mei_CoDi_Conditional_Diffusion_CVPR_2024_supplemental.pdf | null |
Improving Unsupervised Hierarchical Representation with Reinforcement Learning | Ruyi An, Yewen Li, Xu He, Pengjie Gu, Mengchen Zhao, Dong Li, Jianye Hao, Chaojie Wang, Bo An, Mingyuan Zhou | Learning representations to capture the very fundamental understanding of the world is a key challenge in machine learning. The hierarchical structure of explanatory factors hidden in data is such a general representation and could be potentially achieved with a hierarchical VAE. However training a hierarchical VAE always suffers from the "posterior collapse" where the data information is hard to propagate to the higher-level latent variables hence resulting in a bad hierarchical representation. To address this issue we first analyze the shortcomings of existing methods for mitigating the "posterior collapse" from an information theory perspective then highlight the necessity of regularization for explicitly propagating data information to higher-level latent variables while maintaining the dependency between different levels. This naturally leads to formulating the inference of the hierarchical latent representation as a sequential decision process which could benefit from applying reinforcement learning (RL). Aligning RL's objective with the regularization we first introduce a "skip-generative path" to acquire a reward for evaluating the information content of an inferred latent representation and then the developed Q-value function based on it could have a consistent optimization direction of the regularization. Finally policy gradient one of the typical RL methods is employed to train a hierarchical VAE without introducing a gradient estimator. Experimental results firmly support our analysis and demonstrate that our proposed method effectively mitigates the "posterior collapse" issue learns an informative hierarchy acquires explainable latent representations and significantly outperforms other hierarchical VAE-based methods in downstream tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/An_Improving_Unsupervised_Hierarchical_Representation_with_Reinforcement_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/An_Improving_Unsupervised_Hierarchical_Representation_with_Reinforcement_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/An_Improving_Unsupervised_Hierarchical_Representation_with_Reinforcement_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/An_Improving_Unsupervised_Hierarchical_CVPR_2024_supplemental.pdf | null |
HPL-ESS: Hybrid Pseudo-Labeling for Unsupervised Event-based Semantic Segmentation | Linglin Jing, Yiming Ding, Yunpeng Gao, Zhigang Wang, Xu Yan, Dong Wang, Gerald Schaefer, Hui Fang, Bin Zhao, Xuelong Li | Event-based semantic segmentation has gained popularity due to its capability to deal with scenarios under high-speed motion and extreme lighting conditions which cannot be addressed by conventional RGB cameras. Since it is hard to annotate event data previous approaches rely on event-to-image reconstruction to obtain pseudo labels for training. However this will inevitably introduce noise and learning from noisy pseudo labels especially when generated from a single source may reinforce the errors. This drawback is also called confirmation bias in pseudo-labeling. In this paper we propose a novel hybrid pseudo-labeling framework for unsupervised event-based semantic segmentation HPL-ESS to alleviate the influence of noisy pseudo labels. In particular we first employ a plain unsupervised domain adaptation framework as our baseline which can generate a set of pseudo labels through self-training. Then we incorporate offline event-to-image reconstruction into the framework and obtain another set of pseudo labels by predicting segmentation maps on the reconstructed images. A noisy label learning strategy is designed to mix the two sets of pseudo labels and enhance the quality. Moreover we propose a soft prototypical alignment module to further improve the consistency of target domain features. Extensive experiments show that our proposed method outperforms existing state-of-the-art methods by a large margin on the DSEC-Semantic dataset (+5.88% accuracy +10.32% mIoU) which even surpasses several supervised methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jing_HPL-ESS_Hybrid_Pseudo-Labeling_for_Unsupervised_Event-based_Semantic_Segmentation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jing_HPL-ESS_Hybrid_Pseudo-Labeling_for_Unsupervised_Event-based_Semantic_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jing_HPL-ESS_Hybrid_Pseudo-Labeling_for_Unsupervised_Event-based_Semantic_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jing_HPL-ESS_Hybrid_Pseudo-Labeling_CVPR_2024_supplemental.pdf | null |
X-Adapter: Adding Universal Compatibility of Plugins for Upgraded Diffusion Model | Lingmin Ran, Xiaodong Cun, Jia-Wei Liu, Rui Zhao, Song Zijie, Xintao Wang, Jussi Keppo, Mike Zheng Shou | We introduce X-Adapter a universal upgrader to enable the pretrained plug-and-play modules (e.g. ControlNet LoRA) to work directly with the upgraded text-to-image diffusion model (e.g. SDXL) without further retraining. We achieve this goal by training an additional network to control the frozen upgraded model with the new text-image data pairs. In detail X-Adapter keeps a frozen copy of the old model to preserve the connectors of different plugins. Additionally X-Adapter adds trainable mapping layers that bridge the decoders from models of different versions for feature remapping. The remapped features will be used as guidance for the upgraded model. To enhance the guidance ability of X-Adapter we employ a -text training strategy for the upgraded model. After training we also introduce a two-stage denoising strategy to align the initial latents of X-Adapter and the upgraded model. Thanks to our strategies X-Adapter demonstrates universal compatibility with various plugins and also enables plugins of different versions to work together thereby expanding the functionalities of diffusion community. To verify the effectiveness of the proposed method we conduct extensive experiments and the results show that X-Adapter may facilitate wider application in the upgraded foundational diffusion model. Project page at: https://showlab.github.io/X-Adapter. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ran_X-Adapter_Adding_Universal_Compatibility_of_Plugins_for_Upgraded_Diffusion_Model_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ran_X-Adapter_Adding_Universal_Compatibility_of_Plugins_for_Upgraded_Diffusion_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ran_X-Adapter_Adding_Universal_Compatibility_of_Plugins_for_Upgraded_Diffusion_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ran_X-Adapter_Adding_Universal_CVPR_2024_supplemental.pdf | null |
Towards General Robustness Verification of MaxPool-based Convolutional Neural Networks via Tightening Linear Approximation | Yuan Xiao, Shiqing Ma, Juan Zhai, Chunrong Fang, Jinyuan Jia, Zhenyu Chen | The robustness of convolutional neural networks (CNNs) is vital to modern AI-driven systems. It can be quantified by formal verification by providing a certified lower bound within which any perturbation does not alter the original input's classification result. It is challenging due to nonlinear components such as MaxPool. At present many verification methods are sound but risk losing some precision to enhance efficiency and scalability and thus a certified lower bound is a crucial criterion for evaluating the performance of verification tools. In this paper we present MaxLin a robustness verifier for MaxPool-based CNNs with tight Linear approximation. By tightening the linear approximation of the MaxPool function we can certify larger certified lower bounds of CNNs. We evaluate MaxLin with open-sourced benchmarks including LeNet and networks trained on the MNIST CIFAR-10 and Tiny ImageNet datasets. The results show that MaxLin outperforms state-of-the-art tools with up to 110.60% improvement regarding the certified lower bound and 5.13 X speedup for the same neural networks. Our code is available at https://github.com/xiaoyuanpigo/maxlin. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_Towards_General_Robustness_Verification_of_MaxPool-based_Convolutional_Neural_Networks_via_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_Towards_General_Robustness_Verification_of_MaxPool-based_Convolutional_Neural_Networks_via_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_Towards_General_Robustness_Verification_of_MaxPool-based_Convolutional_Neural_Networks_via_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xiao_Towards_General_Robustness_CVPR_2024_supplemental.pdf | null |
BT-Adapter: Video Conversation is Feasible Without Video Instruction Tuning | Ruyang Liu, Chen Li, Yixiao Ge, Thomas H. Li, Ying Shan, Ge Li | The recent progress in Large Language Models (LLM) has spurred various advancements in image-language conversation agents while how to build a proficient video-based dialogue system is still under exploration. Considering the extensive scale of LLM and visual backbone minimal GPU memory is left for facilitating effective temporal modeling which is crucial for comprehending and providing feedback on videos. To this end we propose Branching Temporal Adapter (BT-Adapter) a novel method for extending image-language pretrained models into the video domain. Specifically BT-Adapter serves as a plug-and-use temporal modeling branch alongside the pretrained visual encoder which is tuned while keeping the backbone frozen. Just pretrained once BT-Adapter can be seamlessly integrated into all image conversation models using this version of CLIP enabling video conversations without the need for video instructions. Besides we develop a unique asymmetric token masking strategy inside the branch with tailor-made training tasks for BT-Adapter facilitating faster convergence and better results. Thanks to BT-Adapter we are able to empower existing multimodal dialogue models with strong video understanding capabilities without incurring excessive GPU costs. Without bells and whistles BT-Adapter achieves (1) state-of-the-art zero-shot results on various video tasks using thousands of fewer GPU hours. (2) better performance than current video chatbots without any video instruction tuning. (3) state-of-the-art results of video chatting using video instruction tuning outperforming previous SOTAs by a large margin. The code has been available at https://github.com/farewellthree/BT-Adapter. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_BT-Adapter_Video_Conversation_is_Feasible_Without_Video_Instruction_Tuning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_BT-Adapter_Video_Conversation_is_Feasible_Without_Video_Instruction_Tuning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_BT-Adapter_Video_Conversation_is_Feasible_Without_Video_Instruction_Tuning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_BT-Adapter_Video_Conversation_CVPR_2024_supplemental.pdf | null |
CADTalk: An Algorithm and Benchmark for Semantic Commenting of CAD Programs | Haocheng Yuan, Jing Xu, Hao Pan, Adrien Bousseau, Niloy J. Mitra, Changjian Li | CAD programs are a popular way to compactly encode shapes as a sequence of operations that are easy to parametrically modify. However without sufficient semantic comments and structure such programs can be challenging to understand let alone modify. We introduce the problem of semantic commenting CAD programs wherein the goal is to segment the input program into code blocks corresponding to semantically meaningful shape parts and assign a semantic label to each block. We solve the problem by combining program parsing with visual-semantic analysis afforded by recent advances in foundational language and vision models. Specifically by executing the input programs we create shapes which we use to generate conditional photorealistic images to make use of semantic annotators for such images. We then distill the information across the images and link back to the original programs to semantically comment on them. Additionally we collected and annotated a benchmark dataset CADTalk consisting of 5288 machine-made programs and 45 human-made programs with ground truth semantic comments. We extensively evaluated our approach compared it to a GPT-based baseline and an open-set shape segmentation baseline and reported an 83.24% accuracy on the new CADTalk dataset. Code and data: https://enigma-li.github.io/CADTalk/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yuan_CADTalk_An_Algorithm_and_Benchmark_for_Semantic_Commenting_of_CAD_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16703 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_CADTalk_An_Algorithm_and_Benchmark_for_Semantic_Commenting_of_CAD_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yuan_CADTalk_An_Algorithm_and_Benchmark_for_Semantic_Commenting_of_CAD_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yuan_CADTalk_An_Algorithm_CVPR_2024_supplemental.pdf | null |
Learning to Rematch Mismatched Pairs for Robust Cross-Modal Retrieval | Haochen Han, Qinghua Zheng, Guang Dai, Minnan Luo, Jingdong Wang | Collecting well-matched multimedia datasets is crucial for training cross-modal retrieval models. However in real-world scenarios massive multimodal data are harvested from the Internet which inevitably contains Partially Mismatched Pairs (PMPs). Undoubtedly such semantical irrelevant data will remarkably harm the cross-modal retrieval performance. Previous efforts tend to mitigate this problem by estimating a soft correspondence to down-weight the contribution of PMPs. In this paper we aim to address this challenge from a new perspective: the potential semantic similarity among unpaired samples makes it possible to excavate useful knowledge from mismatched pairs. To achieve this we propose L2RM a general framework based on Optimal Transport (OT) that learns to rematch mismatched pairs. In detail L2RM aims to generate refined alignments by seeking a minimal-cost transport plan across different modalities. To formalize the rematching idea in OT first we propose a self-supervised cost function that automatically learns from explicit similarity-cost mapping relation. Second we present to model a partial OT problem while restricting the transport among false positives to further boost refined alignments. Extensive experiments on three benchmarks demonstrate our L2RM significantly improves the robustness against PMPs for existing models. The code is available at https://github.com/hhc1997/L2RM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Han_Learning_to_Rematch_Mismatched_Pairs_for_Robust_Cross-Modal_Retrieval_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.05105 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Han_Learning_to_Rematch_Mismatched_Pairs_for_Robust_Cross-Modal_Retrieval_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Han_Learning_to_Rematch_Mismatched_Pairs_for_Robust_Cross-Modal_Retrieval_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Han_Learning_to_Rematch_CVPR_2024_supplemental.pdf | null |
Generate Subgoal Images before Act: Unlocking the Chain-of-Thought Reasoning in Diffusion Model for Robot Manipulation with Multimodal Prompts | Fei Ni, Jianye Hao, Shiguang Wu, Longxin Kou, Jiashun Liu, Yan Zheng, Bin Wang, Yuzheng Zhuang | Robotics agents often struggle to understand and follow the multi-modal prompts in complex manipulation scenes which are challenging to be sufficiently and accurately described by text alone. Moreover for long-horizon manipulation tasks the deviation from general instruction tends to accumulate if lack of intermediate guidance from high-level subgoals. For this we consider can we generate subgoal images before act to enhance the instruction following in long-horizon manipulation with multi-modal prompts? Inspired by the great success of diffusion model in image generation tasks we propose a novel hierarchical framework named as CoTDiffusion that incorporates diffusion model as a high-level planner to convert the general and multi-modal prompts into coherent visual subgoal plans which further guide the low-level policy model before action execution. We design a semantic alignment module that can anchor the progress of generated keyframes along a coherent generation chain unlocking the chain-of-thought reasoning ability of diffusion model. Additionally we propose bi-directional generation and frame concat mechanism to further enhance the fidelity of generated subgoal images and the accuracy of instruction following. The experiments cover various robotics manipulation scenarios including visual reasoning visual rearrange and visual constraints. CoTDiffusion achieves outstanding performance gain compared to the baselines without explicit subgoal generation which proves that a subgoal image is worth a thousand words of instruction. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ni_Generate_Subgoal_Images_before_Act_Unlocking_the_Chain-of-Thought_Reasoning_in_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ni_Generate_Subgoal_Images_before_Act_Unlocking_the_Chain-of-Thought_Reasoning_in_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ni_Generate_Subgoal_Images_before_Act_Unlocking_the_Chain-of-Thought_Reasoning_in_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ni_Generate_Subgoal_Images_CVPR_2024_supplemental.pdf | null |
Asymmetric Masked Distillation for Pre-Training Small Foundation Models | Zhiyu Zhao, Bingkun Huang, Sen Xing, Gangshan Wu, Yu Qiao, Limin Wang | Self-supervised foundation models have shown great potential in computer vision thanks to the pre-training paradigm of masked autoencoding. Scale is a primary factor influencing the performance of these foundation models. However these large foundation models often result in high computational cost. This paper focuses on pre-training relatively small vision transformer models that could be efficiently adapted to downstream tasks. Specifically taking inspiration from knowledge distillation in model compression we propose a new asymmetric masked distillation (AMD) framework for pre-training relatively small models with autoencoding. The core of AMD is to devise an asymmetric masking strategy where the teacher model is enabled to see more context information with a lower masking ratio while the student model is still equipped with a high masking ratio. We design customized multi-layer feature alignment between the teacher encoder and student encoder to regularize the pre-training of student MAE. To demonstrate the effectiveness and versatility of AMD we apply it to both ImageMAE and VideoMAE for pre-training relatively small ViT models. AMD achieved 84.6% classification accuracy on IN1K using the ViT-B model. And AMD achieves 73.3% classification accuracy using the ViT-B model on the Something-in-Something V2 dataset a 3.7% improvement over the original ViT-B model from VideoMAE. We also transfer AMD pre-trained models to downstream tasks and obtain consistent performance improvement over the original masked autoencoding. The code and models are available at https://github.com/MCG-NJU/AMD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_Asymmetric_Masked_Distillation_for_Pre-Training_Small_Foundation_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.03149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Asymmetric_Masked_Distillation_for_Pre-Training_Small_Foundation_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Asymmetric_Masked_Distillation_for_Pre-Training_Small_Foundation_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Asymmetric_Masked_Distillation_CVPR_2024_supplemental.pdf | null |
Inversion-Free Image Editing with Language-Guided Diffusion Models | Sihan Xu, Yidong Huang, Jiayi Pan, Ziqiao Ma, Joyce Chai | Despite recent advances in inversion-based editing text-guided image manipulation remains challenging for diffusion models. The primary bottlenecks include 1) the time-consuming nature of the inversion process; 2) the struggle to balance consistency with accuracy; 3) the lack of compatibility with efficient consistency sampling methods used in consistency models. To address the above issues we start by asking ourselves if the inversion process can be eliminated for editing. We show that when the initial sample is known a special variance schedule reduces the denoising step to the same form as the multi-step consistency sampling. We name this Denoising Diffusion Consistent Model (DDCM) and note that it implies a virtual inversion strategy without explicit inversion in sampling. We further unify the attention control mechanisms in a tuning-free framework for text-guided editing. Combining them we present inversion-free editing (InfEdit) which allows for consistent and faithful editing for both rigid and non-rigid semantic changes catering to intricate modifications without compromising on the image's integrity and explicit inversion. Through extensive experiments InfEdit shows strong performance in various editing tasks and also maintains a seamless workflow (less than 3 seconds on one single A40) demonstrating the potential for real-time applications. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Inversion-Free_Image_Editing_with_Language-Guided_Diffusion_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Inversion-Free_Image_Editing_with_Language-Guided_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Inversion-Free_Image_Editing_with_Language-Guided_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Inversion-Free_Image_Editing_CVPR_2024_supplemental.pdf | null |
HumMUSS: Human Motion Understanding using State Space Models | Arnab Mondal, Stefano Alletto, Denis Tome | Understanding human motion from video is essential for a range of applications including pose estimation mesh recovery and action recognition. While state-of-the-art methods predominantly rely on transformer-based architectures these approaches have limitations in practical scenarios. Transformers are slower when sequentially predicting on a continuous stream of frames in real-time and do not generalize to new frame rates. In light of these constraints we propose a novel attention-free spatiotemporal model for human motion understanding building upon recent advancements in state space models. Our model not only matches the performance of transformer-based models in various motion understanding tasks but also brings added benefits like adaptability to different video frame rates and enhanced training speed when working with longer sequence of keypoints. Moreover the proposed model supports both offline and real-time applications. For real-time sequential prediction our model is both memory efficient and several times faster than transformer-based approaches while maintaining their high accuracy. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mondal_HumMUSS_Human_Motion_Understanding_using_State_Space_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.10880 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mondal_HumMUSS_Human_Motion_Understanding_using_State_Space_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mondal_HumMUSS_Human_Motion_Understanding_using_State_Space_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mondal_HumMUSS_Human_Motion_CVPR_2024_supplemental.pdf | null |
MP5: A Multi-modal Open-ended Embodied System in Minecraft via Active Perception | Yiran Qin, Enshen Zhou, Qichang Liu, Zhenfei Yin, Lu Sheng, Ruimao Zhang, Yu Qiao, Jing Shao | It is a long-lasting goal to design an embodied system that can solve long-horizon open-world tasks in human-like ways. However existing approaches usually struggle with compound difficulties caused by the logic-aware decomposition and context-aware execution of these tasks. To this end we introduce MP5 an open-ended multimodal embodied system built upon the challenging Minecraft simulator which can decompose feasible sub-objectives design sophisticated situation-aware plans and perform embodied action control with frequent communication with a goal-conditioned active perception scheme. Specifically MP5 is developed on top of recent advances in Multimodal Large Language Models (MLLMs) and the system is modulated into functional modules that can be scheduled and collaborated to ultimately solve pre-defined context- and process-dependent tasks. Extensive experiments prove that MP5 can achieve a 22% success rate on difficult process-dependent tasks and a 91% success rate on tasks that heavily depend on the context. Moreover MP5 exhibits a remarkable ability to address many open-ended tasks that are entirely novel. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qin_MP5_A_Multi-modal_Open-ended_Embodied_System_in_Minecraft_via_Active_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.07472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qin_MP5_A_Multi-modal_Open-ended_Embodied_System_in_Minecraft_via_Active_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qin_MP5_A_Multi-modal_Open-ended_Embodied_System_in_Minecraft_via_Active_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qin_MP5_A_Multi-modal_CVPR_2024_supplemental.pdf | null |
Uncovering What Why and How: A Comprehensive Benchmark for Causation Understanding of Video Anomaly | Hang Du, Sicheng Zhang, Binzhu Xie, Guoshun Nan, Jiayang Zhang, Junrui Xu, Hangyu Liu, Sicong Leng, Jiangming Liu, Hehe Fan, Dajiu Huang, Jing Feng, Linli Chen, Can Zhang, Xuhuan Li, Hao Zhang, Jianhang Chen, Qimei Cui, Xiaofeng Tao | Video anomaly understanding (VAU) aims to automatically comprehend unusual occurrences in videos thereby enabling various applications such as traffic surveillance and industrial manufacturing. While existing VAU benchmarks primarily concentrate on anomaly detection and localization our focus is on more practicality prompting us to raise the following crucial questions: "what anomaly occurred?" "why did it happen?" and "how severe is this abnormal event?". In pursuit of these answers we present a comprehensive benchmark for Causation Understanding of Video Anomaly (CUVA). Specifically each instance of the proposed benchmark involves three sets of human annotations to indicate the "what" "why" and "how" of an anomaly including 1) anomaly type start and end times and event descriptions 2) natural language explanations for the cause of an anomaly and 3) free text reflecting the effect of the abnormality. In addition we also introduce MMEval a novel evaluation metric designed to better align with human preferences for CUVA facilitating the measurement of existing LLMs in comprehending the underlying cause and corresponding effect of video anomalies. Finally we propose a novel prompt-based method that can serve as a baseline approach for the challenging CUVA. We conduct extensive experiments to show the superiority of our evaluation metric and the prompt-based approach. | https://openaccess.thecvf.com/content/CVPR2024/papers/Du_Uncovering_What_Why_and_How_A_Comprehensive_Benchmark_for_Causation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.00181 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Du_Uncovering_What_Why_and_How_A_Comprehensive_Benchmark_for_Causation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Du_Uncovering_What_Why_and_How_A_Comprehensive_Benchmark_for_Causation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Du_Uncovering_What_Why_CVPR_2024_supplemental.pdf | null |
MiKASA: Multi-Key-Anchor & Scene-Aware Transformer for 3D Visual Grounding | Chun-Peng Chang, Shaoxiang Wang, Alain Pagani, Didier Stricker | 3D visual grounding involves matching natural language descriptions with their corresponding objects in 3D spaces. Existing methods often face challenges with accuracy in object recognition and struggle in interpreting complex linguistic queries particularly with descriptions that involve multiple anchors or are view-dependent. In response we present the MiKASA (Multi-Key-Anchor Scene-Aware) Transformer. Our novel end-to-end trained model integrates a self-attention-based scene-aware object encoder and an original multi-key-anchor technique enhancing object recognition accuracy and the understanding of spatial relationships. Furthermore MiKASA improves the explainability of decision-making facilitating error diagnosis. Our model achieves the highest overall accuracy in the Referit3D challenge for both the Sr3D and Nr3D datasets particularly excelling by a large margin in categories that require viewpoint-dependent descriptions. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chang_MiKASA_Multi-Key-Anchor__Scene-Aware_Transformer_for_3D_Visual_Grounding_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.03077 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chang_MiKASA_Multi-Key-Anchor__Scene-Aware_Transformer_for_3D_Visual_Grounding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chang_MiKASA_Multi-Key-Anchor__Scene-Aware_Transformer_for_3D_Visual_Grounding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chang_MiKASA_Multi-Key-Anchor__CVPR_2024_supplemental.pdf | null |
ZePT: Zero-Shot Pan-Tumor Segmentation via Query-Disentangling and Self-Prompting | Yankai Jiang, Zhongzhen Huang, Rongzhao Zhang, Xiaofan Zhang, Shaoting Zhang | The long-tailed distribution problem in medical image analysis reflects a high prevalence of common conditions and a low prevalence of rare ones which poses a significant challenge in developing a unified model capable of identifying rare or novel tumor categories not encountered during training. In this paper we propose a new Zero-shot Pan-Tumor segmentation framework (ZePT) based on query-disentangling and self-prompting to segment unseen tumor categories beyond the training set. ZePT disentangles the object queries into two subsets and trains them in two stages. Initially it learns a set of fundamental queries for organ segmentation through an object-aware feature grouping strategy which gathers organ-level visual features. Subsequently it refines the other set of advanced queries that focus on the auto-generated visual prompts for unseen tumor segmentation. Moreover we introduce query-knowledge alignment at the feature level to enhance each query's discriminative representation and generalizability. Extensive experiments on various tumor segmentation tasks demonstrate the performance superiority of ZePT which surpasses the previous counterparts and evidences the promising ability for zero-shot tumor segmentation in real-world settings. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jiang_ZePT_Zero-Shot_Pan-Tumor_Segmentation_via_Query-Disentangling_and_Self-Prompting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.04964 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jiang_ZePT_Zero-Shot_Pan-Tumor_Segmentation_via_Query-Disentangling_and_Self-Prompting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jiang_ZePT_Zero-Shot_Pan-Tumor_Segmentation_via_Query-Disentangling_and_Self-Prompting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jiang_ZePT_Zero-Shot_Pan-Tumor_CVPR_2024_supplemental.pdf | null |
Task-Driven Exploration: Decoupling and Inter-Task Feedback for Joint Moment Retrieval and Highlight Detection | Jin Yang, Ping Wei, Huan Li, Ziyang Ren | Video moment retrieval and highlight detection are two highly valuable tasks in video understanding but until recently they have been jointly studied. Although existing studies have made impressive advancement recently they predominantly follow the data-driven bottom-up paradigm. Such paradigm overlooks task-specific and inter-task effects resulting in poor model performance. In this paper we propose a novel task-driven top-down framework TaskWeave for joint moment retrieval and highlight detection. The framework introduces a task-decoupled unit to capture task-specific and common representations. To investigate the interplay between the two tasks we propose an inter-task feedback mechanism which transforms the results of one task as guiding masks to assist the other task. Different from existing methods we present a task-dependent joint loss function to optimize the model. Comprehensive experiments and in-depth ablation studies on QVHighlights TVSum and Charades-STA datasets corroborate the effectiveness and flexibility of the proposed framework. Codes are available at https://github.com/EdenGabriel/TaskWeave. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Task-Driven_Exploration_Decoupling_and_Inter-Task_Feedback_for_Joint_Moment_Retrieval_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.09263 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Task-Driven_Exploration_Decoupling_and_Inter-Task_Feedback_for_Joint_Moment_Retrieval_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Task-Driven_Exploration_Decoupling_and_Inter-Task_Feedback_for_Joint_Moment_Retrieval_CVPR_2024_paper.html | CVPR 2024 | null | null |
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