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ViLa-MIL: Dual-scale Vision-Language Multiple Instance Learning for Whole Slide Image Classification | Jiangbo Shi, Chen Li, Tieliang Gong, Yefeng Zheng, Huazhu Fu | Multiple instance learning (MIL)-based framework has become the mainstream for processing the whole slide image (WSI) with giga-pixel size and hierarchical image context in digital pathology. However these methods heavily depend on a substantial number of bag-level labels and solely learn from the original slides which are easily affected by variations in data distribution. Recently vision language model (VLM)-based methods introduced the language prior by pre-training on large-scale pathological image-text pairs. However the previous text prompt lacks the consideration of pathological prior knowledge therefore does not substantially boost the model's performance. Moreover the collection of such pairs and the pre-training process are very time-consuming and source-intensive. To solve the above problems we propose a dual-scale vision-language multiple instance learning (ViLa-MIL) framework for whole slide image classification. Specifically we propose a dual-scale visual descriptive text prompt based on the frozen large language model (LLM) to boost the performance of VLM effectively. To transfer the VLM to process WSI efficiently for the image branch we propose a prototype-guided patch decoder to aggregate the patch features progressively by grouping similar patches into the same prototype; for the text branch we introduce a context-guided text decoder to enhance the text features by incorporating the multi-granular image contexts. Extensive studies on three multi-cancer and multi-center subtyping datasets demonstrate the superiority of ViLa-MIL. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shi_ViLa-MIL_Dual-scale_Vision-Language_Multiple_Instance_Learning_for_Whole_Slide_Image_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shi_ViLa-MIL_Dual-scale_Vision-Language_Multiple_Instance_Learning_for_Whole_Slide_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shi_ViLa-MIL_Dual-scale_Vision-Language_Multiple_Instance_Learning_for_Whole_Slide_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shi_ViLa-MIL_Dual-scale_Vision-Language_CVPR_2024_supplemental.pdf | null |
Targeted Representation Alignment for Open-World Semi-Supervised Learning | Ruixuan Xiao, Lei Feng, Kai Tang, Junbo Zhao, Yixuan Li, Gang Chen, Haobo Wang | Open-world Semi-Supervised Learning aims to classify unlabeled samples utilizing information from labeled data while unlabeled samples are not only from the labeled known categories but also from novel categories previously unseen. Despite the promise current approaches solely rely on hazardous similarity-based clustering algorithms and give unlabeled samples free rein to spontaneously group into distinct novel class clusters. Nevertheless due to the absence of novel class supervision these methods typically suffer from the representation collapse dilemma---features of different novel categories can get closely intertwined and indistinguishable even collapsing into the same cluster and leading to degraded performance. To alleviate this we propose a novel framework TRAILER which targets to attain an optimal feature arrangement revealed by the recently uncovered neural collapse phenomenon. To fulfill this we adopt targeted prototypes that are pre-assigned uniformly with maximum separation and then progressively align the representations to them. To further tackle the potential downsides of such stringent alignment we encapsulate a sample-target allocation mechanism with coarse-to-fine refinery that is able to infer label assignments with high quality. Extensive experiments demonstrate that TRAILER outperforms current state-of-the-art methods on generic and fine-grained benchmarks. The code is available at https://github.com/Justherozen/TRAILER. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xiao_Targeted_Representation_Alignment_for_Open-World_Semi-Supervised_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_Targeted_Representation_Alignment_for_Open-World_Semi-Supervised_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xiao_Targeted_Representation_Alignment_for_Open-World_Semi-Supervised_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xiao_Targeted_Representation_Alignment_CVPR_2024_supplemental.pdf | null |
Efficient Solution of Point-Line Absolute Pose | Petr Hruby, Timothy Duff, Marc Pollefeys | We revisit certain problems of pose estimation based on 3D--2D correspondences between features which may be points or lines. Specifically we address the two previously-studied minimal problems of estimating camera extrinsics from p \in \ 1 2 \ point--point correspondences and l=3-p line--line correspondences. To the best of our knowledge all of the previously-known practical solutions to these problems required computing the roots of degree \ge 4 (univariate) polynomials when p=2 or degree \ge 8 polynomials when p=1. We describe and implement two elementary solutions which reduce the degrees of the needed polynomials from 4 to 2 and from 8 to 4 respectively. We show experimentally that the resulting solvers are numerically stable and fast: when compared to the previous state-of-the art we may obtain nearly an order of magnitude speedup. The code is available at https://github.com/petrhruby97/efficient_absolute | https://openaccess.thecvf.com/content/CVPR2024/papers/Hruby_Efficient_Solution_of_Point-Line_Absolute_Pose_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.16552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hruby_Efficient_Solution_of_Point-Line_Absolute_Pose_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hruby_Efficient_Solution_of_Point-Line_Absolute_Pose_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hruby_Efficient_Solution_of_CVPR_2024_supplemental.pdf | null |
Text-to-3D using Gaussian Splatting | Zilong Chen, Feng Wang, Yikai Wang, Huaping Liu | Automatic text-to-3D generation that combines Score Distillation Sampling (SDS) with the optimization of volume rendering has achieved remarkable progress in synthesizing realistic 3D objects. Yet most existing text-to-3D methods by SDS and volume rendering suffer from inaccurate geometry e.g. the Janus issue since it is hard to explicitly integrate 3D priors into implicit 3D representations. Besides it is usually time-consuming for them to generate elaborate 3D models with rich colors. In response this paper proposes GSGEN a novel method that adopts Gaussian Splatting a recent state-of-the-art representation to text-to-3D generation. GSGEN aims at generating high-quality 3D objects and addressing existing shortcomings by exploiting the explicit nature of Gaussian Splatting that enables the incorporation of 3D prior. Specifically our method adopts a progressive optimization strategy which includes a geometry optimization stage and an appearance refinement stage. In geometry optimization a coarse representation is established under 3D point cloud diffusion prior along with the ordinary 2D SDS optimization ensuring a sensible and 3D-consistent rough shape. Subsequently the obtained Gaussians undergo an iterative appearance refinement to enrich texture details. In this stage we increase the number of Gaussians by compactness-based densification to enhance continuity and improve fidelity. With these designs our approach can generate 3D assets with delicate details and accurate geometry. Extensive evaluations demonstrate the effectiveness of our method especially for capturing high-frequency components. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Text-to-3D_using_Gaussian_Splatting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.16585 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Text-to-3D_using_Gaussian_Splatting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Text-to-3D_using_Gaussian_Splatting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Text-to-3D_using_Gaussian_CVPR_2024_supplemental.pdf | null |
CapsFusion: Rethinking Image-Text Data at Scale | Qiying Yu, Quan Sun, Xiaosong Zhang, Yufeng Cui, Fan Zhang, Yue Cao, Xinlong Wang, Jingjing Liu | Large multimodal models demonstrate remarkable generalist ability to perform diverse multimodal tasks in a zero-shot manner. Large-scale web-based image-text pairs contribute fundamentally to this success but suffer from excessive noise. Recent studies use alternative captions synthesized by captioning models and have achieved notable benchmark performance. However our experiments reveal significant Scalability Deficiency and World Knowledge Loss issues in models trained with synthetic captions which have been largely obscured by their initial benchmark success. Upon closer examination we identify the root cause as the overly-simplified language structure and lack of knowledge details in existing synthetic captions. To provide higher-quality and more scalable multimodal pretraining data we propose CapsFusion an advanced framework that leverages large language models to consolidate and refine information from both web-based image-text pairs and synthetic captions. Extensive experiments show that CapsFusion captions exhibit remarkable all-round superiority over existing captions in terms of model performance (e.g. 18.8 and 18.3 improvements in CIDEr score on COCO and NoCaps) sample efficiency (requiring 11-16 times less computation than baselines) world knowledge depth and scalability. These effectiveness efficiency and scalability advantages position CapsFusion as a promising candidate for future scaling of LMM training. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_CapsFusion_Rethinking_Image-Text_Data_at_Scale_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.20550 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_CapsFusion_Rethinking_Image-Text_Data_at_Scale_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_CapsFusion_Rethinking_Image-Text_Data_at_Scale_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_CapsFusion_Rethinking_Image-Text_CVPR_2024_supplemental.pdf | null |
On the Content Bias in Frechet Video Distance | Songwei Ge, Aniruddha Mahapatra, Gaurav Parmar, Jun-Yan Zhu, Jia-Bin Huang | Frechet Video Distance (FVD) a prominent metric for evaluating video generation models is known to conflict with human perception occasionally. In this paper we aim to explore the extent of FVD's bias toward frame quality over temporal realism and identify its sources. We first quantify the FVD's sensitivity to the temporal axis by decoupling the frame and motion quality and find that the FVD only increases slightly with larger temporal corruption. We then analyze the generated videos and show that via careful sampling from a large set of generated videos that do not contain motions one can drastically decrease FVD without improving the temporal quality. Both studies suggest FVD's basis towards the quality of individual frames. We show that FVD with features extracted from the recent large-scale self-supervised video models is less biased toward image quality. Finally we revisit a few real-world examples to validate our hypothesis. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ge_On_the_Content_Bias_in_Frechet_Video_Distance_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.12391 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ge_On_the_Content_Bias_in_Frechet_Video_Distance_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ge_On_the_Content_Bias_in_Frechet_Video_Distance_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ge_On_the_Content_CVPR_2024_supplemental.pdf | null |
Tumor Micro-environment Interactions Guided Graph Learning for Survival Analysis of Human Cancers from Whole-slide Pathological Images | Wei Shao, YangYang Shi, Daoqiang Zhang, JunJie Zhou, Peng Wan | The recent advance of deep learning technology brings the possibility of assisting the pathologist to predict the patients' survival from whole-slide pathological images (WSIs). However most of the prevalent methods only worked on the sampled patches in specifically or randomly selected tumor areas of WSIs which has very limited capability to capture the complex interactions between tumor and its surrounding micro-environment components. As a matter of fact tumor is supported and nurtured in the heterogeneous tumor micro-environment(TME) and the detailed analysis of TME and their correlation with tumors are important to in-depth analyze the mechanism of cancer development. In this paper we considered the spatial interactions among tumor and its two major TME components (i.e. lymphocytes and stromal fibrosis) and presented a Tumor Micro-environment Interactions Guided Graph Learning (TMEGL) algorithm for the prognosis prediction of human cancers. Specifically we firstly selected different types of patches as nodes to build graph for each WSI. Then a novel TME neighborhood organization guided graph embedding algorithm was proposed to learn node representations that can preserve their topological structure information. Finally a Gated Graph Attention Network is applied to capture the survival-associated intersections among tumor and different TME components for clinical outcome prediction. We tested TMEGL on three cancer cohorts derived from The Cancer Genome Atlas (TCGA) and the experimental results indicated that TMEGL not only outperforms the existing WSI-based survival analysis models but also has good explainable ability for survival prediction. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shao_Tumor_Micro-environment_Interactions_Guided_Graph_Learning_for_Survival_Analysis_of_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shao_Tumor_Micro-environment_Interactions_Guided_Graph_Learning_for_Survival_Analysis_of_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shao_Tumor_Micro-environment_Interactions_Guided_Graph_Learning_for_Survival_Analysis_of_CVPR_2024_paper.html | CVPR 2024 | null | null |
Towards Generalizable Multi-Object Tracking | Zheng Qin, Le Wang, Sanping Zhou, Panpan Fu, Gang Hua, Wei Tang | Multi-Object Tracking (MOT) encompasses various tracking scenarios each characterized by unique traits. Effective trackers should demonstrate a high degree of generalizability across diverse scenarios. However existing trackers struggle to accommodate all aspects or necessitate hypothesis and experimentation to customize the association information (motion and/or appearance) for a given scenario leading to narrowly tailored solutions with limited generalizability. In this paper we investigate the factors that influence trackers' generalization to different scenarios and concretize them into a set of tracking scenario attributes to guide the design of more generalizable trackers. Furthermore we propose a "point-wise to instance-wise relation" framework for MOT i.e. GeneralTrack which can generalize across diverse scenarios while eliminating the need to balance motion and appearance. Thanks to its superior generalizability our proposed GeneralTrack achieves state-of-the-art performance on multiple benchmarks and demonstrates the potential for domain generalization. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qin_Towards_Generalizable_Multi-Object_Tracking_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qin_Towards_Generalizable_Multi-Object_Tracking_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qin_Towards_Generalizable_Multi-Object_Tracking_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qin_Towards_Generalizable_Multi-Object_CVPR_2024_supplemental.pdf | null |
POPDG: Popular 3D Dance Generation with PopDanceSet | Zhenye Luo, Min Ren, Xuecai Hu, Yongzhen Huang, Li Yao | Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet the first dataset tailored to the preferences of young audiences enabling the generation of aesthetically oriented dances. And it surpasses the AIST++ dataset in music genre diversity and the intricacy and depth of dance movements. Moreover the proposed POPDG model within the iDDPM framework enhances dance diversity and through the Space Augmentation Algorithm strengthens spatial physical connections between human body joints ensuring that increased diversity does not compromise generation quality. A streamlined Alignment Module is also designed to improve the temporal alignment between dance and music. Extensive experiments show that POPDG achieves SOTA results on two datasets. Furthermore the paper also expands on current evaluation metrics. The dataset and code are available at https://github.com/Luke-Luo1/POPDG. | https://openaccess.thecvf.com/content/CVPR2024/papers/Luo_POPDG_Popular_3D_Dance_Generation_with_PopDanceSet_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.03178 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_POPDG_Popular_3D_Dance_Generation_with_PopDanceSet_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_POPDG_Popular_3D_Dance_Generation_with_PopDanceSet_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Luo_POPDG_Popular_3D_CVPR_2024_supplemental.pdf | null |
Image Neural Field Diffusion Models | Yinbo Chen, Oliver Wang, Richard Zhang, Eli Shechtman, Xiaolong Wang, Michael Gharbi | Diffusion models have shown an impressive ability to model complex data distributions with several key advantages over GANs such as stable training better coverage of the training distribution's modes and the ability to solve inverse problems without extra training. However most diffusion models learn the distribution of fixed-resolution images. We propose to learn the distribution of continuous images by training diffusion models on image neural fields which can be rendered at any resolution and show its advantages over fixed-resolution models. To achieve this a key challenge is to obtain a latent space that represents photorealistic image neural fields. We propose a simple and effective method inspired by several recent techniques but with key changes to make the image neural fields photorealistic. Our method can be used to convert existing latent diffusion autoencoders into image neural field autoencoders. We show that image neural field diffusion models can be trained using mixed-resolution image datasets outperform fixed-resolution diffusion models followed by super-resolution models and can solve inverse problems with conditions applied at different scales efficiently. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Image_Neural_Field_Diffusion_Models_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Image_Neural_Field_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Image_Neural_Field_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Image_Neural_Field_CVPR_2024_supplemental.pdf | null |
Discriminative Probing and Tuning for Text-to-Image Generation | Leigang Qu, Wenjie Wang, Yongqi Li, Hanwang Zhang, Liqiang Nie, Tat-Seng Chua | Despite advancements in text-to-image generation (T2I) prior methods often face text-image misalignment problems such as relation confusion in generated images. Existing solutions involve cross-attention manipulation for better compositional understanding or integrating large language models for improved layout planning. However the inherent alignment capabilities of T2I models are still inadequate. By reviewing the link between generative and discriminative modeling we posit that T2I models' discriminative abilities may reflect their text-image alignment proficiency during generation. In this light we advocate bolstering the discriminative abilities of T2I models to achieve more precise text-to-image alignment for generation. We present a discriminative adapter built on T2I models to probe their discriminative abilities on two representative tasks and leverage discriminative fine-tuning to improve their text-image alignment. As a bonus of the discriminative adapter a self-correction mechanism can leverage discriminative gradients to better align generated images to text prompts during inference. Comprehensive evaluations across three benchmark datasets including both in-distribution and out-of-distribution scenarios demonstrate our method's superior generation performance. Meanwhile it achieves state-of-the-art discriminative performance on the two discriminative tasks compared to other generative models. The code is available at https://dpt-t2i.github.io/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qu_Discriminative_Probing_and_Tuning_for_Text-to-Image_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.04321 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qu_Discriminative_Probing_and_Tuning_for_Text-to-Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qu_Discriminative_Probing_and_Tuning_for_Text-to-Image_Generation_CVPR_2024_paper.html | CVPR 2024 | null | null |
Slice3D: Multi-Slice Occlusion-Revealing Single View 3D Reconstruction | Yizhi Wang, Wallace Lira, Wenqi Wang, Ali Mahdavi-Amiri, Hao Zhang | We introduce multi-slice reasoning a new notion for single-view 3D reconstruction which challenges the current and prevailing belief that multi-view synthesis is the most natural conduit between single-view and 3D. Our key observation is that object slicing is a more direct and hence more advantageous means to reveal occluded structures than altering camera views. Specifically slicing can peel through any occluder without obstruction and in the limit (i.e. with infinitely many slices) it is guaranteed to unveil all hidden object parts. We realize our idea by developing Slice3D a novel method for single-view 3D reconstruction which first predicts multi-slice images from a single RGB input image and then integrates the slices into a 3D model using a coordinate-based transformer network to product a signed distance function. The slice images can be regressed or generated both through a U-Net based network. For the former we inject a learnable slice indicator code to designate each decoded image into a spatial slice location while the slice generator is a denoising diffusion model operating on the entirety of slice images stacked on the input channels. We conduct extensive evaluation against state-of-the-art alternatives to demonstrate superiority of our method especially in recovering complex and severely occluded shape structures amid ambiguities. All Slice3D results were produced by networks trained on a single Nvidia A40 GPU with an inference time of less than 20 seconds. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Slice3D_Multi-Slice_Occlusion-Revealing_Single_View_3D_Reconstruction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02221 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Slice3D_Multi-Slice_Occlusion-Revealing_Single_View_3D_Reconstruction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Slice3D_Multi-Slice_Occlusion-Revealing_Single_View_3D_Reconstruction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Slice3D_Multi-Slice_Occlusion-Revealing_CVPR_2024_supplemental.pdf | null |
Towards More Accurate Diffusion Model Acceleration with A Timestep Tuner | Mengfei Xia, Yujun Shen, Changsong Lei, Yu Zhou, Deli Zhao, Ran Yi, Wenping Wang, Yong-Jin Liu | A diffusion model which is formulated to produce an image using thousands of denoising steps usually suffers from a slow inference speed. Existing acceleration algorithms simplify the sampling by skipping most steps yet exhibit considerable performance degradation. By viewing the generation of diffusion models as a discretized integral process we argue that the quality drop is partly caused by applying an inaccurate integral direction to a timestep interval. To rectify this issue we propose a timestep tuner that helps find a more accurate integral direction for a particular interval at the minimum cost. Specifically at each denoising step we replace the original parameterization by conditioning the network on a new timestep enforcing the sampling distribution towards the real one. Extensive experiments show that our plug-in design can be trained efficiently and boost the inference performance of various state-of-the-art acceleration methods especially when there are few denoising steps. For example when using 10 denoising steps on LSUN Bedroom dataset we improve the FID of DDIM from 9.65 to 6.07 simply by adopting our method for a more appropriate set of timesteps. Code is available at \href https://github.com/THU-LYJ-Lab/time-tuner https://github.com/THU-LYJ-Lab/time-tuner . | https://openaccess.thecvf.com/content/CVPR2024/papers/Xia_Towards_More_Accurate_Diffusion_Model_Acceleration_with_A_Timestep_Tuner_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_Towards_More_Accurate_Diffusion_Model_Acceleration_with_A_Timestep_Tuner_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_Towards_More_Accurate_Diffusion_Model_Acceleration_with_A_Timestep_Tuner_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xia_Towards_More_Accurate_CVPR_2024_supplemental.pdf | null |
Rethinking Generalizable Face Anti-spoofing via Hierarchical Prototype-guided Distribution Refinement in Hyperbolic Space | Chengyang Hu, Ke-Yue Zhang, Taiping Yao, Shouhong Ding, Lizhuang Ma | Generalizable face anti-spoofing (FAS) approaches have drawn growing attention due to their robustness for diverse presentation attacks in unseen scenarios. Most previous methods always utilize domain generalization (DG) frameworks via directly aligning diverse source samples into a common feature space. However these methods neglect the hierarchical relations in FAS samples which may hinder the generalization ability by direct alignment. To address these issues we propose a novel Hierarchical Prototype-guided Distribution Refinement (HPDR) framework to learn embedding in hyperbolic space which facilitates the hierarchical relation construction. We also collaborate with prototype learning for hierarchical distribution refinement in hyperbolic space. In detail we propose the Hierarchical Prototype Learning to simultaneously guide domain alignment and improve the discriminative ability via constraining the multi-level relations between prototypes and instances in hyperbolic space. Moreover we design a Prototype-oriented Classifier which further considers relations between the sample and prototypes to improve the robustness of the final decision. Extensive experiments and visualizations demonstrate the effectiveness of our method against previous competitors. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hu_Rethinking_Generalizable_Face_Anti-spoofing_via_Hierarchical_Prototype-guided_Distribution_Refinement_in_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_Rethinking_Generalizable_Face_Anti-spoofing_via_Hierarchical_Prototype-guided_Distribution_Refinement_in_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_Rethinking_Generalizable_Face_Anti-spoofing_via_Hierarchical_Prototype-guided_Distribution_Refinement_in_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hu_Rethinking_Generalizable_Face_CVPR_2024_supplemental.pdf | null |
IIRP-Net: Iterative Inference Residual Pyramid Network for Enhanced Image Registration | Tai Ma, Suwei Zhang, Jiafeng Li, Ying Wen | Deep learning-based image registration (DLIR) methods have achieved remarkable success in deformable image registration. We observe that iterative inference can exploit the well-trained registration network to the fullest extent. In this work we propose a novel Iterative Inference Residual Pyramid Network (IIRP-Net) to enhance registration performance without any additional training costs. In IIRP-Net we construct a streamlined pyramid registration network consisting of a feature extractor and residual flow estimators (RP-Net) to achieve generalized capabilities in feature extraction and registration. Then in the inference phase IIRP-Net employs an iterative inference strategy to enhance RP-Net by iteratively reutilizing residual flow estimators from coarse to fine. The number of iterations is adaptively determined by the proposed IterStop mechanism. We conduct extensive experiments on the FLARE and Mindboggle datasets and the results verify the effectiveness of the proposed method outperforming state-of-the-art deformable image registration methods. Our code is available at https://github.com/Torbjorn1997/IIRP-Net. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ma_IIRP-Net_Iterative_Inference_Residual_Pyramid_Network_for_Enhanced_Image_Registration_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_IIRP-Net_Iterative_Inference_Residual_Pyramid_Network_for_Enhanced_Image_Registration_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_IIRP-Net_Iterative_Inference_Residual_Pyramid_Network_for_Enhanced_Image_Registration_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ma_IIRP-Net_Iterative_Inference_CVPR_2024_supplemental.pdf | null |
Learning without Exact Guidance: Updating Large-scale High-resolution Land Cover Maps from Low-resolution Historical Labels | Zhuohong Li, Wei He, Jiepan Li, Fangxiao Lu, Hongyan Zhang | Large-scale high-resolution (HR) land-cover mapping is a vital task to survey the Earth's surface and resolve many challenges facing humanity. However it is still a non-trivial task hindered by complex ground details various landforms and the scarcity of accurate training labels over a wide-span geographic area. In this paper we propose an efficient weakly supervised framework (Paraformer) to guide large-scale HR land-cover mapping with easy-access historical land-cover data of low resolution (LR). Specifically existing land-cover mapping approaches reveal the dominance of CNNs in preserving local ground details but still suffer from insufficient global modeling in various landforms. Therefore we design a parallel CNN-Transformer feature extractor in Paraformer consisting of a downsampling-free CNN branch and a Transformer branch to jointly capture local and global contextual information. Besides facing the spatial mismatch of training data a pseudo-label-assisted training (PLAT) module is adopted to reasonably refine LR labels for weakly supervised semantic segmentation of HR images. Experiments on two large-scale datasets demonstrate the superiority of Paraformer over other state-of-the-art methods for automatically updating HR land-cover maps from LR historical labels. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Learning_without_Exact_Guidance_Updating_Large-scale_High-resolution_Land_Cover_Maps_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.02746 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Learning_without_Exact_Guidance_Updating_Large-scale_High-resolution_Land_Cover_Maps_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Learning_without_Exact_Guidance_Updating_Large-scale_High-resolution_Land_Cover_Maps_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Learning_without_Exact_CVPR_2024_supplemental.pdf | null |
GenesisTex: Adapting Image Denoising Diffusion to Texture Space | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_GenesisTex_Adapting_Image_Denoising_Diffusion_to_Texture_Space_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_GenesisTex_Adapting_Image_Denoising_Diffusion_to_Texture_Space_CVPR_2024_paper.html | CVPR 2024 | null | null |
TTA-EVF: Test-Time Adaptation for Event-based Video Frame Interpolation via Reliable Pixel and Sample Estimation | Hoonhee Cho, Taewoo Kim, Yuhwan Jeong, Kuk-Jin Yoon | Video Frame Interpolation (VFI) which aims at generating high-frame-rate videos from low-frame-rate inputs is a highly challenging task. The emergence of bio-inspired sensors known as event cameras which boast microsecond-level temporal resolution has ushered in a transformative era for VFI. Nonetheless the application of event-based VFI techniques in domains with distinct environments from the training data can be problematic. This is mainly because event camera data distribution can undergo substantial variations based on camera settings and scene conditions presenting challenges for effective adaptation. In this paper we propose a test-time adaptation method for event-based VFI to address the gap between the source and target domains. Our approach enables sequential learning in an online manner on the target domain which only provides low-frame-rate videos. We present an approach that leverages confident pixels as pseudo ground-truths enabling stable and accurate online learning from low-frame-rate videos. Furthermore to prevent overfitting during the continuous online process where the same scene is encountered repeatedly we propose a method of blending historical samples with current scenes. Extensive experiments validate the effectiveness of our method both in cross-domain and continuous domain shifting setups. The code is available at https://github.com/Chohoonhee/TTA-EVF. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cho_TTA-EVF_Test-Time_Adaptation_for_Event-based_Video_Frame_Interpolation_via_Reliable_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cho_TTA-EVF_Test-Time_Adaptation_for_Event-based_Video_Frame_Interpolation_via_Reliable_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cho_TTA-EVF_Test-Time_Adaptation_for_Event-based_Video_Frame_Interpolation_via_Reliable_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cho_TTA-EVF_Test-Time_Adaptation_CVPR_2024_supplemental.zip | null |
Image-to-Image Matching via Foundation Models: A New Perspective for Open-Vocabulary Semantic Segmentation | Yuan Wang, Rui Sun, Naisong Luo, Yuwen Pan, Tianzhu Zhang | Open-vocabulary semantic segmentation (OVS) aims to segment images of arbitrary categories specified by class labels or captions. However most previous best-performing methods whether pixel grouping methods or region recognition methods suffer from false matches between image features and category labels. We attribute this to the natural gap between the textual features and visual features. In this work we rethink how to mitigate false matches from the perspective of image-to-image matching and propose a novel relation-aware intra-modal matching (RIM) framework for OVS based on visual foundation models. RIM achieves robust region classification by firstly constructing diverse image-modal reference features and then matching them with region features based on relation-aware ranking distribution. The proposed RIM enjoys several merits. First the intra-modal reference features are better aligned circumventing potential ambiguities that may arise in cross-modal matching. Second the ranking-based matching process harnesses the structure information implicit in the inter-class relationships making it more robust than comparing individually. Extensive experiments on three benchmarks demonstrate that RIM outperforms previous state-of-the-art methods by large margins obtaining a lead of more than 10% in mIoU on PASCAL VOC benchmark | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Image-to-Image_Matching_via_Foundation_Models_A_New_Perspective_for_Open-Vocabulary_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00262 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Image-to-Image_Matching_via_Foundation_Models_A_New_Perspective_for_Open-Vocabulary_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Image-to-Image_Matching_via_Foundation_Models_A_New_Perspective_for_Open-Vocabulary_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Image-to-Image_Matching_via_CVPR_2024_supplemental.pdf | null |
BigGait: Learning Gait Representation You Want by Large Vision Models | Dingqiang Ye, Chao Fan, Jingzhe Ma, Xiaoming Liu, Shiqi Yu | Gait recognition stands as one of the most pivotal remote identification technologies and progressively expands across research and industry communities. However existing gait recognition methods heavily rely on task-specific upstream driven by supervised learning to provide explicit gait representations like silhouette sequences which inevitably introduce expensive annotation costs and potential error accumulation. Escaping from this trend this work explores effective gait representations based on the all-purpose knowledge produced by task-agnostic Large Vision Models (LVMs) and proposes a simple yet efficient gait framework termed BigGait. Specifically the Gait Representation Extractor (GRE) within BigGait draws upon design principles from established gait representations effectively transforming all-purpose knowledge into implicit gait representations without requiring third-party supervision signals. Experiments on CCPG CAISA-B* and SUSTech1K indicate that BigGait significantly outperforms the previous methods in both within-domain and cross-domain tasks in most cases and provides a more practical paradigm for learning the next-generation gait representation. Finally we delve into prospective challenges and promising directions in LVMs-based gait recognition aiming to inspire future work in this emerging topic. The source code is available at https://github.com/ShiqiYu/OpenGait. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ye_BigGait_Learning_Gait_Representation_You_Want_by_Large_Vision_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.19122 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ye_BigGait_Learning_Gait_Representation_You_Want_by_Large_Vision_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ye_BigGait_Learning_Gait_Representation_You_Want_by_Large_Vision_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ye_BigGait_Learning_Gait_CVPR_2024_supplemental.pdf | null |
BEVNeXt: Reviving Dense BEV Frameworks for 3D Object Detection | Zhenxin Li, Shiyi Lan, Jose M. Alvarez, Zuxuan Wu | Recently the rise of query-based Transformer decoders is reshaping camera-based 3D object detection. These query-based decoders are surpassing the traditional dense BEV (Bird's Eye View)-based methods. However we argue that dense BEV frameworks remain important due to their outstanding abilities in depth estimation and object localization depicting 3D scenes accurately and comprehensively. This paper aims to address the drawbacks of the existing dense BEV-based 3D object detectors by introducing our proposed enhanced components including a CRF-modulated depth estimation module enforcing object-level consistencies a long-term temporal aggregation module with extended receptive fields and a two-stage object decoder combining perspective techniques with CRF-modulated depth embedding. These enhancements lead to a "modernized" dense BEV framework dubbed BEVNeXt. On the nuScenes benchmark BEVNeXt outperforms both BEV-based and query-based frameworks under various settings achieving a state-of-the-art result of 64.2 NDS on the nuScenes test set. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_BEVNeXt_Reviving_Dense_BEV_Frameworks_for_3D_Object_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.01696 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_BEVNeXt_Reviving_Dense_BEV_Frameworks_for_3D_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_BEVNeXt_Reviving_Dense_BEV_Frameworks_for_3D_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | null | null |
SNIFFER: Multimodal Large Language Model for Explainable Out-of-Context Misinformation Detection | Peng Qi, Zehong Yan, Wynne Hsu, Mong Li Lee | Misinformation is a prevalent societal issue due to its potential high risks. Out-Of-Context (OOC) misinformation where authentic images are repurposed with false text is one of the easiest and most effective ways to mislead audiences. Current methods focus on assessing image-text consistency but lack convincing explanations for their judgments which are essential for debunking misinformation. While Multimodal Large Language Models (MLLMs) have rich knowledge and innate capability for visual reasoning and explanation generation they still lack sophistication in understanding and discovering the subtle cross-modal differences. In this paper we introduce Sniffer a novel multimodal large language model specifically engineered for OOC misinformation detection and explanation. Sniffer employs two-stage instruction tuning on InstructBLIP. The first stage refines the model's concept alignment of generic objects with news-domain entities and the second stage leverages OOC-specific instruction data generated by language-only GPT-4 to fine-tune the model's discriminatory powers. Enhanced by external tools and retrieval Sniffer not only detects inconsistencies between text and image but also utilizes external knowledge for contextual verification. Our experiments show that Sniffer surpasses the original MLLM by over 40% and outperforms state-of-the-art methods in detection accuracy. Sniffer also provides accurate and persuasive explanations as validated by quantitative and human evaluations. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qi_SNIFFER_Multimodal_Large_Language_Model_for_Explainable_Out-of-Context_Misinformation_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.03170 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qi_SNIFFER_Multimodal_Large_Language_Model_for_Explainable_Out-of-Context_Misinformation_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qi_SNIFFER_Multimodal_Large_Language_Model_for_Explainable_Out-of-Context_Misinformation_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qi_SNIFFER_Multimodal_Large_CVPR_2024_supplemental.pdf | null |
Beyond Seen Primitive Concepts and Attribute-Object Compositional Learning | Nirat Saini, Khoi Pham, Abhinav Shrivastava | Learning from seen attribute-object pairs to generalize to unseen compositions has been studied extensively in Compositional Zero-Shot Learning (CZSL). However CZSL setup is still limited to seen attributes and objects and cannot generalize to unseen concepts and their compositions. To overcome this limitation we propose a new task Open Vocabulary-Compositional Zero-shot Learning (OV-CZSL) where unseen attributes objects and unseen compositions are evaluated. To show that OV-CZSL is a challenging yet solvable problem we propose three new benchmarks based on existing datasets MIT-States C-GQA and VAW-CZSL along with new baselines and evaluation setup. We use language embeddings and external vocabulary with our novel neighborhood expansion loss to allow any method to learn semantic correlations between seen and unseen primitives. | https://openaccess.thecvf.com/content/CVPR2024/papers/Saini_Beyond_Seen_Primitive_Concepts_and_Attribute-Object_Compositional_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Saini_Beyond_Seen_Primitive_Concepts_and_Attribute-Object_Compositional_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Saini_Beyond_Seen_Primitive_Concepts_and_Attribute-Object_Compositional_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Saini_Beyond_Seen_Primitive_CVPR_2024_supplemental.pdf | null |
Unleashing Network Potentials for Semantic Scene Completion | Fengyun Wang, Qianru Sun, Dong Zhang, Jinhui Tang | Semantic scene completion (SSC) aims to predict complete 3D voxel occupancy and semantics from a single-view RGB-D image and recent SSC methods commonly adopt multi-modal inputs. However our investigation reveals two limitations: ineffective feature learning from single modalities and overfitting to limited datasets. To address these issues this paper proposes a novel SSC framework - Adversarial Modality Modulation Network (AMMNet) - with a fresh perspective of optimizing gradient updates. The proposed AMMNet introduces two core modules: a cross-modal modulation enabling the interdependence of gradient flows between modalities and a customized adversarial training scheme leveraging dynamic gradient competition. Specifically the cross-modal modulation adaptively re-calibrates the features to better excite representation potentials from each single modality. The adversarial training employs a minimax game of evolving gradients with customized guidance to strengthen the generator's perception of visual fidelity from both geometric completeness and semantic correctness. Extensive experimental results demonstrate that AMMNet outperforms state-of-the-art SSC methods by a large margin providing a promising direction for improving the effectiveness and generalization of SSC methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Unleashing_Network_Potentials_for_Semantic_Scene_Completion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.07560 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Unleashing_Network_Potentials_for_Semantic_Scene_Completion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Unleashing_Network_Potentials_for_Semantic_Scene_Completion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Unleashing_Network_Potentials_CVPR_2024_supplemental.pdf | null |
HOIST-Former: Hand-held Objects Identification Segmentation and Tracking in the Wild | Supreeth Narasimhaswamy, Huy Anh Nguyen, Lihan Huang, Minh Hoai | We address the challenging task of identifying segmenting and tracking hand-held objects which is crucial for applications such as human action segmentation and performance evaluation. This task is particularly challenging due to heavy occlusion rapid motion and the transitory nature of objects being hand-held where an object may be held released and subsequently picked up again. To tackle these challenges we have developed a novel transformer-based architecture called HOIST-Former. HOIST-Former is adept at spatially and temporally segmenting hands and objects by iteratively pooling features from each other ensuring that the processes of identification segmentation and tracking of hand-held objects depend on the hands' positions and their contextual appearance. We further refine HOIST-Former with a contact loss that focuses on areas where hands are in contact with objects. Moreover we also contribute an in-the-wild video dataset called HOIST which comprises 4125 videos complete with bounding boxes segmentation masks and tracking IDs for hand-held objects. Through experiments on the HOIST dataset and two additional public datasets we demonstrate the efficacy of HOIST-Former in segmenting and tracking hand-held objects. | https://openaccess.thecvf.com/content/CVPR2024/papers/Narasimhaswamy_HOIST-Former_Hand-held_Objects_Identification_Segmentation_and_Tracking_in_the_Wild_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Narasimhaswamy_HOIST-Former_Hand-held_Objects_Identification_Segmentation_and_Tracking_in_the_Wild_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Narasimhaswamy_HOIST-Former_Hand-held_Objects_Identification_Segmentation_and_Tracking_in_the_Wild_CVPR_2024_paper.html | CVPR 2024 | null | null |
Contextrast: Contextual Contrastive Learning for Semantic Segmentation | Changki Sung, Wanhee Kim, Jungho An, Wooju Lee, Hyungtae Lim, Hyun Myung | Despite great improvements in semantic segmentation challenges persist because of the lack of local/global contexts and the relationship between them. In this paper we propose Contextrast a contrastive learning-based semantic segmentation method that allows to capture local/global contexts and comprehend their relationships. Our proposed method comprises two parts: a) contextual contrastive learning (CCL) and b) boundary-aware negative (BANE) sampling. Contextual contrastive learning obtains local/global context from multi-scale feature aggregation and inter/intra-relationship of features for better discrimination capabilities. Meanwhile BANE sampling selects embedding features along the boundaries of incorrectly predicted regions to employ them as harder negative samples on our contrastive learning resolving segmentation issues along the boundary region by exploiting fine-grained details. We demonstrate that our Contextrast substantially enhances the performance of semantic segmentation networks outperforming state-of-the-art contrastive learning approaches on diverse public datasets e.g. Cityscapes CamVid PASCAL-C COCO-Stuff and ADE20K without an increase in computational cost during inference. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sung_Contextrast_Contextual_Contrastive_Learning_for_Semantic_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.10633 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sung_Contextrast_Contextual_Contrastive_Learning_for_Semantic_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sung_Contextrast_Contextual_Contrastive_Learning_for_Semantic_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sung_Contextrast_Contextual_Contrastive_CVPR_2024_supplemental.pdf | null |
Learning Occupancy for Monocular 3D Object Detection | Liang Peng, Junkai Xu, Haoran Cheng, Zheng Yang, Xiaopei Wu, Wei Qian, Wenxiao Wang, Boxi Wu, Deng Cai | Monocular 3D detection is a challenging task due to the lack of accurate 3D information. Existing approaches typically rely on geometry constraints and dense depth estimates to facilitate the learning but often fail to fully exploit the benefits of three-dimensional feature extraction in frustum and 3D space. In this paper we propose OccupancyM3D a method of learning occupancy for monocular 3D detection. It directly learns occupancy in frustum and 3D space leading to more discriminative and informative 3D features and representations. Specifically by using synchronized raw sparse LiDAR point clouds we define the space status and generate voxel-based occupancy labels. We formulate occupancy prediction as a simple classification problem and design associated occupancy losses. Resulting occupancy estimates are employed to enhance original frustum/3D features. As a result experiments on KITTI and Waymo open datasets demonstrate that the proposed method achieves a new state of the art and surpasses other methods by a significant margin. | https://openaccess.thecvf.com/content/CVPR2024/papers/Peng_Learning_Occupancy_for_Monocular_3D_Object_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2305.15694 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_Learning_Occupancy_for_Monocular_3D_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_Learning_Occupancy_for_Monocular_3D_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Peng_Learning_Occupancy_for_CVPR_2024_supplemental.pdf | null |
LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection | Dat Nguyen, Nesryne Mejri, Inder Pal Singh, Polina Kuleshova, Marcella Astrid, Anis Kacem, Enjie Ghorbel, Djamila Aouada | This paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Network (LAA-Net). Existing methods for high-quality deepfake detection are mainly based on a supervised binary classifier coupled with an implicit attention mechanism. As a result they do not generalize well to unseen manipulations. To handle this issue two main contributions are made. First an explicit attention mechanism within a multi-task learning framework is proposed. By combining heatmap-based and self-consistency attention strategies LAA-Net is forced to focus on a few small artifact-prone vulnerable regions. Second an Enhanced Feature Pyramid Network (E-FPN) is proposed as a simple and effective mechanism for spreading discriminative low-level features into the final feature output with the advantage of limiting redundancy. Experiments performed on several benchmarks show the superiority of our approach in terms of Area Under the Curve (AUC) and Average Precision (AP). The code is available at https://github.com/10Ring/LAA-Net. | https://openaccess.thecvf.com/content/CVPR2024/papers/Nguyen_LAA-Net_Localized_Artifact_Attention_Network_for_Quality-Agnostic_and_Generalizable_Deepfake_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Nguyen_LAA-Net_Localized_Artifact_Attention_Network_for_Quality-Agnostic_and_Generalizable_Deepfake_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Nguyen_LAA-Net_Localized_Artifact_Attention_Network_for_Quality-Agnostic_and_Generalizable_Deepfake_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Nguyen_LAA-Net_Localized_Artifact_CVPR_2024_supplemental.pdf | null |
LEAD: Learning Decomposition for Source-free Universal Domain Adaptation | Sanqing Qu, Tianpei Zou, Lianghua He, Florian Röhrbein, Alois Knoll, Guang Chen, Changjun Jiang | Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence of both covariate and label shifts. Recently Source-free Universal Domain Adaptation (SF-UniDA) has emerged to achieve UniDA without access to source data which tends to be more practical due to data protection policies. The main challenge lies in determining whether covariate-shifted samples belong to target-private unknown categories. Existing methods tackle this either through hand-crafted thresholding or by developing time-consuming iterative clustering strategies. In this paper we propose a new idea of LEArning Decomposition (LEAD) which decouples features into source-known and -unknown components to identify target-private data. Technically LEAD initially leverages the orthogonal decomposition analysis for feature decomposition. Then LEAD builds instance-level decision boundaries to adaptively identify target-private data. Extensive experiments across various UniDA scenarios have demonstrated the effectiveness and superiority of LEAD. Notably in the OPDA scenario on VisDA dataset LEAD outperforms GLC by 3.5% overall H-score and reduces 75% time to derive pseudo-labeling decision boundaries. Besides LEAD is also appealing in that it is complementary to most existing methods. The code is available at https://github. com/ispc-lab/LEAD | https://openaccess.thecvf.com/content/CVPR2024/papers/Qu_LEAD_Learning_Decomposition_for_Source-free_Universal_Domain_Adaptation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.03421 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qu_LEAD_Learning_Decomposition_for_Source-free_Universal_Domain_Adaptation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qu_LEAD_Learning_Decomposition_for_Source-free_Universal_Domain_Adaptation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qu_LEAD_Learning_Decomposition_CVPR_2024_supplemental.pdf | null |
AUEditNet: Dual-Branch Facial Action Unit Intensity Manipulation with Implicit Disentanglement | Shiwei Jin, Zhen Wang, Lei Wang, Peng Liu, Ning Bi, Truong Nguyen | Facial action unit (AU) intensity plays a pivotal role in quantifying fine-grained expression behaviors which is an effective condition for facial expression manipulation. However publicly available datasets containing intensity annotations for multiple AUs remain severely limited often featuring a restricted number of subjects. This limitation places challenges to the AU intensity manipulation in images due to disentanglement issues leading researchers to resort to other large datasets with pretrained AU intensity estimators for pseudo labels. In addressing this constraint and fully leveraging manual annotations of AU intensities for precise manipulation we introduce AUEditNet. Our proposed model achieves impressive intensity manipulation across 12 AUs trained effectively with only 18 subjects. Utilizing a dual-branch architecture our approach achieves comprehensive disentanglement of facial attributes and identity without necessitating additional loss functions or implementing with large batch sizes. This approach offers a potential solution to achieve desired facial attribute editing despite the dataset's limited subject count. Our experiments demonstrate AUEditNet's superior accuracy in editing AU intensities affirming its capability in disentangling facial attributes and identity within a limited subject pool. AUEditNet allows conditioning by either intensity values or target images eliminating the need for constructing AU combinations for specific facial expression synthesis. Moreover AU intensity estimation as a downstream task validates the consistency between real and edited images confirming the effectiveness of our proposed AU intensity manipulation method. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jin_AUEditNet_Dual-Branch_Facial_Action_Unit_Intensity_Manipulation_with_Implicit_Disentanglement_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.05063 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jin_AUEditNet_Dual-Branch_Facial_Action_Unit_Intensity_Manipulation_with_Implicit_Disentanglement_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jin_AUEditNet_Dual-Branch_Facial_Action_Unit_Intensity_Manipulation_with_Implicit_Disentanglement_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jin_AUEditNet_Dual-Branch_Facial_CVPR_2024_supplemental.zip | null |
BodyMAP - Jointly Predicting Body Mesh and 3D Applied Pressure Map for People in Bed | Abhishek Tandon, Anujraaj Goyal, Henry M. Clever, Zackory Erickson | Accurately predicting the 3D human posture and the pressure exerted on the body for people resting in bed visualized as a body mesh (3D pose & shape) with a 3D pressure map holds significant promise for healthcare applications particularly in the prevention of pressure ulcers. Current methods focus on singular facets of the problem---predicting only 2D/3D poses generating 2D pressure images predicting pressure only for certain body regions instead of the full body or forming indirect approximations to the 3D pressure map. In contrast we introduce BodyMAP which jointly predicts the human body mesh and 3D applied pressure map across the entire human body. Our network leverages multiple visual modalities incorporating both a depth image of a person in bed and its corresponding 2D pressure image acquired from a pressure-sensing mattress. The 3D pressure map is represented as a pressure value at each mesh vertex and thus allows for precise localization of high-pressure regions on the body. Additionally we present BodyMAP-WS a new formulation of pressure prediction in which we implicitly learn pressure in 3D by aligning sensed 2D pressure images with a differentiable 2D projection of the predicted 3D pressure maps. In evaluations with real-world human data our method outperforms the current state-of-the-art technique by 25% on both body mesh and 3D applied pressure map prediction tasks for people in bed. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tandon_BodyMAP_-_Jointly_Predicting_Body_Mesh_and_3D_Applied_Pressure_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tandon_BodyMAP_-_Jointly_Predicting_Body_Mesh_and_3D_Applied_Pressure_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tandon_BodyMAP_-_Jointly_Predicting_Body_Mesh_and_3D_Applied_Pressure_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tandon_BodyMAP_-_Jointly_CVPR_2024_supplemental.zip | null |
OneLLM: One Framework to Align All Modalities with Language | Jiaming Han, Kaixiong Gong, Yiyuan Zhang, Jiaqi Wang, Kaipeng Zhang, Dahua Lin, Yu Qiao, Peng Gao, Xiangyu Yue | Multimodal large language models (MLLMs) have gained significant attention due to their strong multimodal understanding capability. However existing works rely heavily on modality-specific encoders which usually differ in architecture and are limited to common modalities. In this paper we present OneLLM an MLLM that aligns eight modalities to language using a unified framework. We achieve this through a unified multimodal encoder and a progressive multimodal alignment pipeline. In detail we first train an image projection module to connect a vision encoder with LLM. Then we build a universal projection module (UPM) by mixing multiple image projection modules and dynamic routing. Finally we progressively align more modalities to LLM with the UPM. To fully leverage the potential of OneLLM in following instructions we also curated a comprehensive multimodal instruction dataset including 2M items from image audio video point cloud depth/normal map IMU and fMRI brain activity. OneLLM is evaluated on 25 diverse benchmarks encompassing tasks such as multimodal captioning question answering and reasoning where it delivers excellent performance. Code data model and online demo are available at https://github.com/csuhan/OneLLM | https://openaccess.thecvf.com/content/CVPR2024/papers/Han_OneLLM_One_Framework_to_Align_All_Modalities_with_Language_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.03700 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Han_OneLLM_One_Framework_to_Align_All_Modalities_with_Language_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Han_OneLLM_One_Framework_to_Align_All_Modalities_with_Language_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Han_OneLLM_One_Framework_CVPR_2024_supplemental.pdf | null |
PAD: Patch-Agnostic Defense against Adversarial Patch Attacks | Lihua Jing, Rui Wang, Wenqi Ren, Xin Dong, Cong Zou | Adversarial patch attacks present a significant threat to real-world object detectors due to their practical feasibility. Existing defense methods which rely on attack data or prior knowledge struggle to effectively address a wide range of adversarial patches. In this paper we show two inherent characteristics of adversarial patches semantic independence and spatial heterogeneity independent of their appearance shape size quantity and location. Semantic independence indicates that adversarial patches operate autonomously within their semantic context while spatial heterogeneity manifests as distinct image quality of the patch area that differs from original clean image due to the independent generation process. Based on these observations we propose PAD a novel adversarial patch localization and removal method that does not require prior knowledge or additional training. PAD offers patch-agnostic defense against various adversarial patches compatible with any pre-trained object detectors. Our comprehensive digital and physical experiments involving diverse patch types such as localized noise printable and naturalistic patches exhibit notable improvements over state-of-the-art works. Our code is available at https://github.com/Lihua-Jing/PAD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jing_PAD_Patch-Agnostic_Defense_against_Adversarial_Patch_Attacks_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.16452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jing_PAD_Patch-Agnostic_Defense_against_Adversarial_Patch_Attacks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jing_PAD_Patch-Agnostic_Defense_against_Adversarial_Patch_Attacks_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jing_PAD_Patch-Agnostic_Defense_CVPR_2024_supplemental.pdf | null |
MULAN: A Multi Layer Annotated Dataset for Controllable Text-to-Image Generation | Petru-Daniel Tudosiu, Yongxin Yang, Shifeng Zhang, Fei Chen, Steven McDonagh, Gerasimos Lampouras, Ignacio Iacobacci, Sarah Parisot | Text-to-image generation has achieved astonishing results yet precise spatial controllability and prompt fidelity remain highly challenging. This limitation is typically addressed through cumbersome prompt engineering scene layout conditioning or image editing techniques which often require hand drawn masks. Nonetheless pre-existing works struggle to take advantage of the natural instance-level compositionality of scenes due to the typically flat nature of rasterized RGB output images. Towards addressing this challenge we introduce MuLAn: a novel dataset comprising over 44K MUlti-Layer ANnotations of RGB images as multi-layer instance-wise RGBA decompositions and over 100K instance images. To build MuLAn we developed a training free pipeline which decomposes a monocular RGB image into a stack of RGBA layers comprising of background and isolated instances. We achieve this through the use of pretrained general-purpose models and by developing three modules: image decomposition for instance discovery and extraction instance completion to reconstruct occluded areas and image re-assembly. We use our pipeline to create MuLAn-COCO and MuLAn-LAION datasets which contain a variety of image decompositions in terms of style composition and complexity. With MuLAn we provide the first photorealistic resource providing instance decomposition and occlusion information for high quality images opening up new avenues for text-to-image generative AI research. With this we aim to encourage the development of novel generation and editing technology in particular layer-wise solutions. MuLAn data resources are available at https://MuLAn-dataset.github.io/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Tudosiu_MULAN_A_Multi_Layer_Annotated_Dataset_for_Controllable_Text-to-Image_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.02790 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tudosiu_MULAN_A_Multi_Layer_Annotated_Dataset_for_Controllable_Text-to-Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tudosiu_MULAN_A_Multi_Layer_Annotated_Dataset_for_Controllable_Text-to-Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tudosiu_MULAN_A_Multi_CVPR_2024_supplemental.pdf | null |
Rotation-Agnostic Image Representation Learning for Digital Pathology | Saghir Alfasly, Abubakr Shafique, Peyman Nejat, Jibran Khan, Areej Alsaafin, Ghazal Alabtah, H.R. Tizhoosh | This paper addresses complex challenges in histopathological image analysis through three key contributions. Firstly it introduces a fast patch selection method FPS for whole-slide image (WSI) analysis significantly reducing computational cost while maintaining accuracy. Secondly it presents PathDino a lightweight histopathology feature extractor with a minimal configuration of five Transformer blocks and only ? 9 million parameters markedly fewer than alternatives. Thirdly it introduces a rotation-agnostic representation learning paradigm using self-supervised learning effectively mitigating overfitting. We also show that our compact model outperforms existing state-of-the-art histopathology-specific vision transformers on 12 diverse datasets including both internal datasets spanning four sites (breast liver skin and colorectal) and seven public datasets (PANDA CAMELYON16 BRACS DigestPath Kather PanNuke and WSSS4LUAD). Notably even with a training dataset of ? 6 million histopathology patches from The Cancer Genome Atlas (TCGA) our approach demonstrates an average 8.5% improvement in patch-level majority vote performance. These contributions provide a robust framework for enhancing image analysis in digital pathology rigorously validated through extensive evaluation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Alfasly_Rotation-Agnostic_Image_Representation_Learning_for_Digital_Pathology_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.08359 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Alfasly_Rotation-Agnostic_Image_Representation_Learning_for_Digital_Pathology_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Alfasly_Rotation-Agnostic_Image_Representation_Learning_for_Digital_Pathology_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Alfasly_Rotation-Agnostic_Image_Representation_CVPR_2024_supplemental.pdf | null |
Unbiased Faster R-CNN for Single-source Domain Generalized Object Detection | Yajing Liu, Shijun Zhou, Xiyao Liu, Chunhui Hao, Baojie Fan, Jiandong Tian | Single-source domain generalization (SDG) for object detection is a challenging yet essential task as the distribution bias of the unseen domain degrades the algorithm performance significantly. However existing methods attempt to extract domain-invariant features neglecting that the biased data leads the network to learn biased features that are non-causal and poorly generalizable. To this end we propose an Unbiased Faster R-CNN (UFR) for generalizable feature learning. Specifically we formulate SDG in object detection from a causal perspective and construct a Structural Causal Model (SCM) to analyze the data bias and feature bias in the task which are caused by scene confounders and object attribute confounders. Based on the SCM we design a Global-Local Transformation module for data augmentation which effectively simulates domain diversity and mitigates the data bias. Additionally we introduce a Causal Attention Learning module that incorporates a designed attention invariance loss to learn image-level features that are robust to scene confounders. Moreover we develop a Causal Prototype Learning module with an explicit instance constraint and an implicit prototype constraint which further alleviates the negative impact of object attribute confounders. Experimental results on five scenes demonstrate the prominent generalization ability of our method with an improvement of 3.9% mAP on the Night-Clear scene. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Unbiased_Faster_R-CNN_for_Single-source_Domain_Generalized_Object_Detection_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Unbiased_Faster_R-CNN_for_Single-source_Domain_Generalized_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Unbiased_Faster_R-CNN_for_Single-source_Domain_Generalized_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Unbiased_Faster_R-CNN_CVPR_2024_supplemental.pdf | null |
Super-Resolution Reconstruction from Bayer-Pattern Spike Streams | Yanchen Dong, Ruiqin Xiong, Jian Zhang, Zhaofei Yu, Xiaopeng Fan, Shuyuan Zhu, Tiejun Huang | Spike camera is a neuromorphic vision sensor that can capture highly dynamic scenes by generating a continuous stream of binary spikes to represent the arrival of photons at very high temporal resolution. Equipped with Bayer color filter array (CFA) color spike camera (CSC) has been invented to capture color information. Although spike camera has already demonstrated great potential for high-speed imaging its spatial resolution is limited compared with conventional digital cameras. This paper proposes a Color Spike Camera Super-Resolution (CSCSR) network to super-resolve higher-resolution color images from spike camera streams with Bayer CFA. To be specific we first propose a representation for Bayer-pattern spike streams exploring local temporal information with global perception to represent the binary data. Then we exploit the CFA layout and sub-pixel level motion to collect temporal pixels for the spatial super-resolution of each color channel. In particular a residual-based module for feature refinement is developed to reduce the impact of motion estimation errors. Considering color correlation we jointly utilize the multi-stage temporal-pixel features of color channels to reconstruct the high-resolution color image. Experimental results demonstrate that the proposed scheme can reconstruct satisfactory color images with both high temporal and spatial resolution from low-resolution Bayer-pattern spike streams. The source codes are available at https://github.com/csycdong/CSCSR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dong_Super-Resolution_Reconstruction_from_Bayer-Pattern_Spike_Streams_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_Super-Resolution_Reconstruction_from_Bayer-Pattern_Spike_Streams_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_Super-Resolution_Reconstruction_from_Bayer-Pattern_Spike_Streams_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dong_Super-Resolution_Reconstruction_from_CVPR_2024_supplemental.pdf | null |
EASE-DETR: Easing the Competition among Object Queries | Yulu Gao, Yifan Sun, Xudong Ding, Chuyang Zhao, Si Liu | This paper views the DETR's non-duplicate detection ability as a competition result among object queries. Around each object there are usually multiple queries within which only a single one can win the chance to become the final detection. Such a competition is hard: while some competing queries initially have very close prediction scores their leading query has to dramatically enlarge its score superiority after several decoder layers. To help the leading query stands out this paper proposes EASE-DETR which eases the competition by introducing bias that favours the leading one. EASE-DETR is very simple: in every intermediate decoder layer we identify the "leading / trailing" relationship between any two queries and encode this binary relationship into the following decoder layer to amplify the superiority of the leading one. More concretely the leading query is to be protected from mutual query suppression in the self-attention layer and encouraged to absorb more object features in the cross-attention layer therefore accelerating to win. Experimental results show that EASE-DETR brings consistent and remarkable improvement to various DETRs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gao_EASE-DETR_Easing_the_Competition_among_Object_Queries_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_EASE-DETR_Easing_the_Competition_among_Object_Queries_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gao_EASE-DETR_Easing_the_Competition_among_Object_Queries_CVPR_2024_paper.html | CVPR 2024 | null | null |
KPConvX: Modernizing Kernel Point Convolution with Kernel Attention | Hugues Thomas, Yao-Hung Hubert Tsai, Timothy D. Barfoot, Jian Zhang | In the field of deep point cloud understanding KPConv is a unique architecture that uses kernel points to locate convolutional weights in space instead of relying on Multi-Layer Perceptron (MLP) encodings. While it initially achieved success it has since been surpassed by recent MLP networks that employ updated designs and training strategies. Building upon the kernel point principle we present two novel designs: KPConvD (depthwise KPConv) a lighter design that enables the use of deeper architectures and KPConvX an innovative design that scales the depthwise convolutional weights of KPConvD with kernel attention values. Using KPConvX with a modern architecture and training strategy we are able to outperform current state-of-the-art approaches on the ScanObjectNN Scannetv2 and S3DIS datasets. We validate our design choices through ablation studies and release our code and models. | https://openaccess.thecvf.com/content/CVPR2024/papers/Thomas_KPConvX_Modernizing_Kernel_Point_Convolution_with_Kernel_Attention_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.13194 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Thomas_KPConvX_Modernizing_Kernel_Point_Convolution_with_Kernel_Attention_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Thomas_KPConvX_Modernizing_Kernel_Point_Convolution_with_Kernel_Attention_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Thomas_KPConvX_Modernizing_Kernel_CVPR_2024_supplemental.pdf | null |
Clockwork Diffusion: Efficient Generation With Model-Step Distillation | Amirhossein Habibian, Amir Ghodrati, Noor Fathima, Guillaume Sautiere, Risheek Garrepalli, Fatih Porikli, Jens Petersen | This work aims to improve the efficiency of text-to-image diffusion models. While diffusion models use computationally expensive UNet-based denoising operations in every generation step we identify that not all operations are equally relevant for the final output quality. In particular we observe that UNet layers operating on high-res feature maps are relatively sensitive to small perturbations. In contrast low-res feature maps influence the semantic layout of the final image and can often be perturbed with no noticeable change in the output. Based on this observation we propose Clockwork Diffusion a method that periodically reuses computation from preceding denoising steps to approximate low-res feature maps at one or more subsequent steps. For multiple base- lines and for both text-to-image generation and image editing we demonstrate that Clockwork leads to comparable or improved perceptual scores with drastically reduced computational complexity. As an example for Stable Diffusion v1.5 with 8 DPM++ steps we save 32% of FLOPs with negligible FID and CLIP change. We re- lease code at https://github.com/Qualcomm-AI-research/clockwork-diffusion | https://openaccess.thecvf.com/content/CVPR2024/papers/Habibian_Clockwork_Diffusion_Efficient_Generation_With_Model-Step_Distillation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.08128 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Habibian_Clockwork_Diffusion_Efficient_Generation_With_Model-Step_Distillation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Habibian_Clockwork_Diffusion_Efficient_Generation_With_Model-Step_Distillation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Habibian_Clockwork_Diffusion_Efficient_CVPR_2024_supplemental.pdf | null |
Pick-or-Mix: Dynamic Channel Sampling for ConvNets | Ashish Kumar, Daneul Kim, Jaesik Park, Laxmidhar Behera | Channel pruning approaches for convolutional neural networks (ConvNets) deactivate the channels statically or dynamically and require special implementation. In addition channel squeezing in representative ConvNets is carried out via 1 x 1 convolutions which dominates a large portion of computations and network parameters. Given these challenges we propose an effective multi-purpose module for dynamic channel sampling namely Pick-or-Mix (PiX) which does not require special implementation. PiX divides a set of channels into subsets and then picks from them where the picking decision is dynamically made per each pixel based on the input activations. We plug PiX into prominent ConvNet architectures and verify its multi-purpose utilities. After replacing 1 x 1 channel squeezing layers in ResNet with PiX the network becomes 25% faster without losing accuracy. We show that PiX allows ConvNets to learn better data representation than widely adopted approaches to enhance networks' representation power (e.g. SE CBAM AFF SKNet and DWP). We also show that PiX achieves state-of-the-art performance on network downscaling and dynamic channel pruning applications. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kumar_Pick-or-Mix_Dynamic_Channel_Sampling_for_ConvNets_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kumar_Pick-or-Mix_Dynamic_Channel_Sampling_for_ConvNets_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kumar_Pick-or-Mix_Dynamic_Channel_Sampling_for_ConvNets_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kumar_Pick-or-Mix_Dynamic_Channel_CVPR_2024_supplemental.pdf | null |
Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation | Hang Li, Chengzhi Shen, Philip Torr, Volker Tresp, Jindong Gu | Diffusion-based models have gained significant popularity for text-to-image generation due to their exceptional image-generation capabilities. A risk with these models is the potential generation of inappropriate content such as biased or harmful images. However the underlying reasons for generating such undesired content from the perspective of the diffusion model's internal representation remain unclear. Previous work interprets vectors in an interpretable latent space of diffusion models as semantic concepts. However existing approaches cannot discover directions for arbitrary concepts such as those related to inappropriate concepts. In this work we propose a novel self-supervised approach to find interpretable latent directions for a given concept. With the discovered vectors we further propose a simple approach to mitigate inappropriate generation. Extensive experiments have been conducted to verify the effectiveness of our mitigation approach namely for fair generation safe generation and responsible text-enhancing generation. Project page: https://interpretdiffusion.github.io. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Self-Discovering_Interpretable_Diffusion_Latent_Directions_for_Responsible_Text-to-Image_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17216 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Self-Discovering_Interpretable_Diffusion_Latent_Directions_for_Responsible_Text-to-Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Self-Discovering_Interpretable_Diffusion_Latent_Directions_for_Responsible_Text-to-Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Self-Discovering_Interpretable_Diffusion_CVPR_2024_supplemental.pdf | null |
HiLo: Detailed and Robust 3D Clothed Human Reconstruction with High-and Low-Frequency Information of Parametric Models | Yifan Yang, Dong Liu, Shuhai Zhang, Zeshuai Deng, Zixiong Huang, Mingkui Tan | Reconstructing 3D clothed human involves creating a detailed geometry of individuals in clothing with applications ranging from virtual try-on movies to games. To enable practical and widespread applications recent advances propose to generate a clothed human from an RGB image. However they struggle to reconstruct detailed and robust avatars simultaneously. We empirically find that the high-frequency (HF) and low-frequency (LF) information from a parametric model has the potential to enhance geometry details and improve robustness to noise respectively. Based on this we propose HiLo namely clothed human reconstruction with high- and low-frequency information which contains two components. 1) To recover detailed geometry using HF information we propose a progressive HF Signed Distance Function to enhance the detailed 3D geometry of a clothed human. We analyze that our progressive learning manner alleviates large gradients that hinder model convergence. 2) To achieve robust reconstruction against inaccurate estimation of the parametric model by using LF information we propose a spatial interaction implicit function. This function effectively exploits the complementary spatial information from a low-resolution voxel grid of the parametric model. Experimental results demonstrate that HiLo outperforms the state-of-the-art methods by 10.43% and 9.54% in terms of Chamfer distance on the Thuman2.0 and CAPE datasets respectively. Additionally HiLo demonstrates robustness to noise from the parametric model challenging poses and various clothing styles. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_HiLo_Detailed_and_Robust_3D_Clothed_Human_Reconstruction_with_High-and_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.04876 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_HiLo_Detailed_and_Robust_3D_Clothed_Human_Reconstruction_with_High-and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_HiLo_Detailed_and_Robust_3D_Clothed_Human_Reconstruction_with_High-and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_HiLo_Detailed_and_CVPR_2024_supplemental.pdf | null |
Promptable Behaviors: Personalizing Multi-Objective Rewards from Human Preferences | Minyoung Hwang, Luca Weihs, Chanwoo Park, Kimin Lee, Aniruddha Kembhavi, Kiana Ehsani | Customizing robotic behaviors to be aligned with diverse human preferences is an underexplored challenge in the field of embodied AI. In this paper we present Promptable Behaviors a novel framework that facilitates efficient personalization of robotic agents to diverse human preferences in complex environments. We use multi-objective reinforcement learning to train a single policy adaptable to a broad spectrum of preferences. We introduce three distinct methods to infer human preferences by leveraging different types of interactions: (1) human demonstrations (2) preference feedback on trajectory comparisons and (3) language instructions. We evaluate the proposed method in personalized object-goal navigation and flee navigation tasks in ProcTHOR and RoboTHOR demonstrating the ability to prompt agent behaviors to satisfy human preferences in various scenarios. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hwang_Promptable_Behaviors_Personalizing_Multi-Objective_Rewards_from_Human_Preferences_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.09337 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hwang_Promptable_Behaviors_Personalizing_Multi-Objective_Rewards_from_Human_Preferences_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hwang_Promptable_Behaviors_Personalizing_Multi-Objective_Rewards_from_Human_Preferences_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hwang_Promptable_Behaviors_Personalizing_CVPR_2024_supplemental.pdf | null |
Stationary Representations: Optimally Approximating Compatibility and Implications for Improved Model Replacements | Niccolò Biondi, Federico Pernici, Simone Ricci, Alberto Del Bimbo | Learning compatible representations enables the interchangeable use of semantic features as models are updated over time. This is particularly relevant in search and retrieval systems where it is crucial to avoid reprocessing of the gallery images with the updated model. While recent research has shown promising empirical evidence there is still a lack of comprehensive theoretical understanding about learning compatible representations. In this paper we demonstrate that the stationary representations learned by the d-Simplex fixed classifier optimally approximate compatibility representation according to the two inequality constraints of its formal definition. This not only establishes a solid foundation for future works in this line of research but also presents implications that can be exploited in practical learning scenarios. An exemplary application is the now-standard practice of downloading and fine-tuning new pre-trained models. Specifically we show the strengths and critical issues of stationary representations in the case in which a model undergoing sequential fine-tuning is asynchronously replaced by downloading a better-performing model pre-trained elsewhere. Such a representation enables seamless delivery of retrieval service (i.e. no reprocessing of gallery images) and offers improved performance without operational disruptions during model replacement. Code available at: https://github.com/miccunifi/iamcl2r. | https://openaccess.thecvf.com/content/CVPR2024/papers/Biondi_Stationary_Representations_Optimally_Approximating_Compatibility_and_Implications_for_Improved_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.02581 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Biondi_Stationary_Representations_Optimally_Approximating_Compatibility_and_Implications_for_Improved_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Biondi_Stationary_Representations_Optimally_Approximating_Compatibility_and_Implications_for_Improved_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Biondi_Stationary_Representations_Optimally_CVPR_2024_supplemental.pdf | null |
Towards Calibrated Multi-label Deep Neural Networks | Jiacheng Cheng, Nuno Vasconcelos | The problem of calibrating deep neural networks (DNNs) for multi-label learning is considered. It is well-known that DNNs trained by cross-entropy for single-label or one-hot classification are poorly calibrated. Many calibration techniques have been proposed to address the problem. However little attention has been paid to the calibration of multi-label DNNs. In this literature the focus has been on improving labeling accuracy in the face of severe dataset unbalance. This is addressed by the introduction of asymmetric losses which have became very popular. However these losses do not induce well calibrated classifiers. In this work we first provide a theoretical explanation for this poor calibration performance by showing that these loses losses lack the strictly proper property a necessary condition for accurate probability estimation. To overcome this problem we propose a new Strictly Proper Asymmetric (SPA) loss. This is complemented by a Label Pair Regularizer (LPR) that increases the number of calibration constraints introduced per training example. The effectiveness of both contributions is validated by extensive experiments on various multi-label datasets. The resulting training method is shown to significantly decrease the calibration error while maintaining state-of-the-art accuracy. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cheng_Towards_Calibrated_Multi-label_Deep_Neural_Networks_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cheng_Towards_Calibrated_Multi-label_Deep_Neural_Networks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cheng_Towards_Calibrated_Multi-label_Deep_Neural_Networks_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cheng_Towards_Calibrated_Multi-label_CVPR_2024_supplemental.pdf | null |
SceneTex: High-Quality Texture Synthesis for Indoor Scenes via Diffusion Priors | Dave Zhenyu Chen, Haoxuan Li, Hsin-Ying Lee, Sergey Tulyakov, Matthias Nießner | We propose SceneTex a novel method for effectively generating high-quality and style-consistent textures for indoor scenes using depth-to-image diffusion priors. Unlike previous methods that either iteratively warp 2D views onto a mesh surface or distillate diffusion latent features without accurate geometric and style cues SceneTex formulates the texture synthesis task as an optimization problem in the RGB space where style and geometry consistency are properly reflected. At its core SceneTex proposes a multiresolution texture field to implicitly encode the mesh appearance. We optimize the target texture via a score-distillation-based objective function in respective RGB renderings. To further secure the style consistency across views we introduce a cross-attention decoder to predict the RGB values by cross-attending to the pre-sampled reference locations in each instance. SceneTex enables various and accurate texture synthesis for 3D-FRONT scenes demonstrating significant improvements in visual quality and prompt fidelity over the prior texture generation methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_SceneTex_High-Quality_Texture_Synthesis_for_Indoor_Scenes_via_Diffusion_Priors_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17261 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_SceneTex_High-Quality_Texture_Synthesis_for_Indoor_Scenes_via_Diffusion_Priors_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_SceneTex_High-Quality_Texture_Synthesis_for_Indoor_Scenes_via_Diffusion_Priors_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_SceneTex_High-Quality_Texture_CVPR_2024_supplemental.pdf | null |
Neural Underwater Scene Representation | Yunkai Tang, Chengxuan Zhu, Renjie Wan, Chao Xu, Boxin Shi | Among the numerous efforts towards digitally recovering the physical world Neural Radiance Fields (NeRFs) have proved effective in most cases. However underwater scene introduces unique challenges due to the absorbing water medium the local change in lighting and the dynamic contents in the scene. We aim at developing a neural underwater scene representation for these challenges modeling the complex process of attenuation unstable in-scattering and moving objects during light transport. The proposed method can reconstruct the scenes from both established datasets and in-the-wild videos with outstanding fidelity. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tang_Neural_Underwater_Scene_Representation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_Neural_Underwater_Scene_Representation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_Neural_Underwater_Scene_Representation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tang_Neural_Underwater_Scene_CVPR_2024_supplemental.pdf | null |
Progress-Aware Online Action Segmentation for Egocentric Procedural Task Videos | Yuhan Shen, Ehsan Elhamifar | We address the problem of online action segmentation for egocentric procedural task videos. While previous studies have mostly focused on offline action segmentation where entire videos are available for both training and inference the transition to online action segmentation is crucial for practical applications like AR/VR task assistants. Notably applying an offline-trained model directly to online inference results in a significant performance drop due to the inconsistency between training and inference. We propose an online action segmentation framework by first modifying existing architectures to make them causal. Second we develop a novel action progress prediction module to dynamically estimate the progress of ongoing actions and using them to refine the predictions of causal action segmentation. Third we propose to learn task graphs from training videos and leverage them to obtain smooth and procedure-consistent segmentations. With the combination of progress and task graph with casual action segmentation our framework effectively addresses prediction uncertainty and oversegmentation in online action segmentation and achieves significant improvement on three egocentric datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shen_Progress-Aware_Online_Action_Segmentation_for_Egocentric_Procedural_Task_Videos_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shen_Progress-Aware_Online_Action_Segmentation_for_Egocentric_Procedural_Task_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shen_Progress-Aware_Online_Action_Segmentation_for_Egocentric_Procedural_Task_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shen_Progress-Aware_Online_Action_CVPR_2024_supplemental.pdf | null |
TUMTraf V2X Cooperative Perception Dataset | Walter Zimmer, Gerhard Arya Wardana, Suren Sritharan, Xingcheng Zhou, Rui Song, Alois C. Knoll | Cooperative perception offers several benefits for enhancing the capabilities of autonomous vehicles and improving road safety. Using roadside sensors in addition to onboard sensors increases reliability and extends the sensor range. External sensors offer higher situational awareness for automated vehicles and prevent occlusions. We propose CoopDet3D a cooperative multi-modal fusion model and TUMTraf-V2X a perception dataset for the cooperative 3D object detection and tracking task. Our dataset contains 2000 labeled point clouds and 5000 labeled images from five roadside and four onboard sensors. It includes 30k 3D boxes with track IDs and precise GPS and IMU data. We labeled nine categories and covered occlusion scenarios with challenging driving maneuvers like traffic violations near-miss events overtaking and U-turns. Through multiple experiments we show that our CoopDet3D camera-LiDAR fusion model achieves an increase of +14.36 3D mAP compared to a vehicle camera-LiDAR fusion model. Finally we make our dataset model labeling tool and devkit publicly available on our website: https://tum-traffic-dataset.github.io/tumtraf-v2x. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zimmer_TUMTraf_V2X_Cooperative_Perception_Dataset_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01316 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zimmer_TUMTraf_V2X_Cooperative_Perception_Dataset_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zimmer_TUMTraf_V2X_Cooperative_Perception_Dataset_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zimmer_TUMTraf_V2X_Cooperative_CVPR_2024_supplemental.pdf | null |
Constrained Layout Generation with Factor Graphs | Mohammed Haroon Dupty, Yanfei Dong, Sicong Leng, Guoji Fu, Yong Liang Goh, Wei Lu, Wee Sun Lee | This paper addresses the challenge of object-centric layout generation under spatial constraints seen in multiple domains including floorplan design process. The design process typically involves specifying a set of spatial constraints that include object attributes like size and inter-object relations such as relative positioning. Existing works which typically represent objects as single nodes lack the granularity to accurately model complex interactions between objects. For instance often only certain parts of an object like a room's right wall interact with adjacent objects. To address this gap we introduce a factor graph based approach with four latent variable nodes for each room and a factor node for each constraint. The factor nodes represent dependencies among the variables to which they are connected effectively capturing constraints that are potentially of a higher order. We then develop message-passing on the bipartite graph forming a factor graph neural network that is trained to produce a floorplan that aligns with the desired requirements. Our approach is simple and generates layouts faithful to the user requirements demonstrated by a large improvement in IOU scores over existing methods. Additionally our approach being inferential and accurate is well-suited to the practical human-in-the-loop design process where specifications evolve iteratively offering a practical and powerful tool for AI-guided design. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dupty_Constrained_Layout_Generation_with_Factor_Graphs_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.00385 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dupty_Constrained_Layout_Generation_with_Factor_Graphs_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dupty_Constrained_Layout_Generation_with_Factor_Graphs_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dupty_Constrained_Layout_Generation_CVPR_2024_supplemental.pdf | null |
SLICE: Stabilized LIME for Consistent Explanations for Image Classification | Revoti Prasad Bora, Philipp Terhörst, Raymond Veldhuis, Raghavendra Ramachandra, Kiran Raja | Local Interpretable Model-agnostic Explanations (LIME) - a widely used post-ad-hoc model agnostic explainable AI (XAI) technique. It works by training a simple transparent (surrogate) model using random samples drawn around the neighborhood of the instance (image) to be explained (IE). Explanations are then extracted for a black-box model and a given IE using the surrogate model. However the explanations of LIME suffer from inconsistency across different runs for the same model and the same IE. We identify two main types of inconsistencies: variance in the sign and importance ranks of the segments (superpixels). These factors hinder LIME from obtaining consistent explanations. We analyze these inconsistencies and propose a new method Stabilized LIME for Consistent Explanations (SLICE). The proposed method handles the stabilization problem in two aspects: using a novel feature selection technique to eliminate spurious superpixels and an adaptive perturbation technique to generate perturbed images in the neighborhood of IE. Our results demonstrate that the explanations from SLICE exhibit significantly better consistency and fidelity than LIME (and its variant BayLime). | https://openaccess.thecvf.com/content/CVPR2024/papers/Bora_SLICE_Stabilized_LIME_for_Consistent_Explanations_for_Image_Classification_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bora_SLICE_Stabilized_LIME_for_Consistent_Explanations_for_Image_Classification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bora_SLICE_Stabilized_LIME_for_Consistent_Explanations_for_Image_Classification_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bora_SLICE_Stabilized_LIME_CVPR_2024_supplemental.pdf | null |
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly Detection | Jiawen Zhu, Choubo Ding, Yu Tian, Guansong Pang | Open-set supervised anomaly detection (OSAD) - a recently emerging anomaly detection area - aims at utilizing a few samples of anomaly classes seen during training to detect unseen anomalies (i.e. samples from open-set anomaly classes) while effectively identifying the seen anomalies. Benefiting from the prior knowledge illustrated by the seen anomalies current OSAD methods can often largely reduce false positive errors. However these methods are trained in a closed-set setting and treat the anomaly examples as from a homogeneous distribution rendering them less effective in generalizing to unseen anomalies that can be drawn from any distribution. This paper proposes to learn heterogeneous anomaly distributions using the limited anomaly examples to address this issue. To this end we introduce a novel approach namely Anomaly Heterogeneity Learning (AHL) that simulates a diverse set of heterogeneous anomaly distributions and then utilizes them to learn a unified heterogeneous abnormality model in surrogate open-set environments. Further AHL is a generic framework that existing OSAD models can plug and play for enhancing their abnormality modeling. Extensive experiments on nine real-world anomaly detection datasets show that AHL can 1) substantially enhance different state-of-the-art OSAD models in detecting seen and unseen anomalies and 2) effectively generalize to unseen anomalies in new domains. Code is available at https://github.com/mala-lab/AHL. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Anomaly_Heterogeneity_Learning_for_Open-set_Supervised_Anomaly_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.12790 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Anomaly_Heterogeneity_Learning_for_Open-set_Supervised_Anomaly_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Anomaly_Heterogeneity_Learning_for_Open-set_Supervised_Anomaly_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_Anomaly_Heterogeneity_Learning_CVPR_2024_supplemental.pdf | null |
SPECAT: SPatial-spEctral Cumulative-Attention Transformer for High-Resolution Hyperspectral Image Reconstruction | Zhiyang Yao, Shuyang Liu, Xiaoyun Yuan, Lu Fang | Compressive spectral image reconstruction is a critical method for acquiring images with high spatial and spectral resolution. Current advanced methods which involve designing deeper networks or adding more self-attention modules are limited by the scope of attention modules and the irrelevance of attentions across different dimensions. This leads to difficulties in capturing non-local mutation features in the spatial-spectral domain and results in a significant parameter increase but only limited performance improvement. To address these issues we propose SPECAT a SPatial-spEctral Cumulative-Attention Transformer designed for high-resolution hyperspectral image reconstruction. SPECAT utilizes Cumulative-Attention Blocks (CABs) within an efficient hierarchical framework to extract features from non-local spatial-spectral details. Furthermore it employs a projection-object Dual-domain Loss Function (DLF) to integrate the optical path constraint a physical aspect often overlooked in current methodologies. Ultimately SPECAT not only significantly enhances the reconstruction quality of spectral details but also breaks through the bottleneck of mutual restriction between the cost and accuracy in existing algorithms. Our experimental results demonstrate the superiority of SPECAT achieving 40.3 dB in hyperspectral reconstruction benchmarks outperforming the state-of-the-art (SOTA) algorithms by 1.2 dB while using only 5% of the network parameters and 10% of the computational cost. The code is available at https://github.com/THU-luvision/SPECAT. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yao_SPECAT_SPatial-spEctral_Cumulative-Attention_Transformer_for_High-Resolution_Hyperspectral_Image_Reconstruction_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yao_SPECAT_SPatial-spEctral_Cumulative-Attention_Transformer_for_High-Resolution_Hyperspectral_Image_Reconstruction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yao_SPECAT_SPatial-spEctral_Cumulative-Attention_Transformer_for_High-Resolution_Hyperspectral_Image_Reconstruction_CVPR_2024_paper.html | CVPR 2024 | null | null |
Attentive Illumination Decomposition Model for Multi-Illuminant White Balancing | Dongyoung Kim, Jinwoo Kim, Junsang Yu, Seon Joo Kim | White balance (WB) algorithms in many commercial cameras assume single and uniform illumination leading to undesirable results when multiple lighting sources with different chromaticities exist in the scene. Prior research on multi-illuminant WB typically predicts illumination at the pixel level without fully grasping the scene's actual lighting conditions including the number and color of light sources. This often results in unnatural outcomes lacking in overall consistency. To handle this problem we present a deep white balancing model that leverages the slot attention where each slot is in charge of representing individual illuminants. This design enables the model to generate chromaticities and weight maps for individual illuminants which are then fused to compose the final illumination map. Furthermore we propose the centroid-matching loss which regulates the activation of each slot based on the color range thereby enhancing the model to separate illumination more effectively. Our method achieves the state-of-the-art performance on both single- and multi-illuminant WB benchmarks and also offers additional information such as the number of illuminants in the scene and their chromaticity. This capability allows for illumination editing an application not feasible with prior methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Attentive_Illumination_Decomposition_Model_for_Multi-Illuminant_White_Balancing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18277 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Attentive_Illumination_Decomposition_Model_for_Multi-Illuminant_White_Balancing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Attentive_Illumination_Decomposition_Model_for_Multi-Illuminant_White_Balancing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Attentive_Illumination_Decomposition_CVPR_2024_supplemental.pdf | null |
Efficient Stitchable Task Adaptation | Haoyu He, Zizheng Pan, Jing Liu, Jianfei Cai, Bohan Zhuang | The paradigm of pre-training and fine-tuning has laid the foundation for deploying deep learning models. However most fine-tuning methods are designed to meet a specific resource budget. Recently considering diverse deployment scenarios with various resource budgets SN-Net is introduced to quickly obtain numerous new networks (stitches) from the pre-trained models (anchors) in a model family via model stitching. Although promising SN-Net confronts new challenges when adapting it to new target domains including huge memory and storage requirements and a long and sub-optimal multistage adaptation process. In this work we present a novel framework Efficient Stitchable Task Adaptation (ESTA) to efficiently produce a palette of fine-tuned models that adhere to diverse resource constraints. Specifically we first tailor parameter-efficient fine-tuning to share low-rank updates among the stitches while maintaining independent bias terms. In this way we largely reduce fine-tuning memory burdens and mitigate the interference among stitches that arises in task adaptation. Furthermore we streamline a simple yet effective one-stage deployment pipeline which estimates the important stitches to deploy with training-time gradient statistics. By assigning higher sampling probabilities to important stitches we also get a boosted Pareto frontier. Extensive experiments on 25 downstream visual recognition tasks demonstrate that our ESTA is capable of generating stitches with smooth accuracy-efficiency trade-offs and surpasses the direct SN-Net adaptation by remarkable margins with significantly lower training time and fewer trainable parameters. Furthermore we demonstrate the flexibility and scalability of our ESTA framework by stitching LLMs from LLaMA family obtaining chatbot stitches of assorted sizes. | https://openaccess.thecvf.com/content/CVPR2024/papers/He_Efficient_Stitchable_Task_Adaptation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17352 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/He_Efficient_Stitchable_Task_Adaptation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/He_Efficient_Stitchable_Task_Adaptation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/He_Efficient_Stitchable_Task_CVPR_2024_supplemental.pdf | null |
Image Processing GNN: Breaking Rigidity in Super-Resolution | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Tian_Image_Processing_GNN_Breaking_Rigidity_in_Super-Resolution_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tian_Image_Processing_GNN_Breaking_Rigidity_in_Super-Resolution_CVPR_2024_paper.html | CVPR 2024 | null | null |
Revisiting Counterfactual Problems in Referring Expression Comprehension | Zhihan Yu, Ruifan Li | Traditional referring expression comprehension (REC) aims to locate the target referent in an image guided by a text query. Several previous methods have studied on the Counterfactual problem in REC (C-REC) where the objects for a given query cannot be found in the image. However these methods focus on the overall image-text or specific attribute mismatch only. In this paper we address the C-REC problem from a deep perspective of fine-grained attributes. To this aim we first propose a fine-grained counterfactual sample generation method to construct C-REC datasets. Specifically we leverage pre-trained language model such as BERT to modify the attribute words in the queries obtaining the corresponding counterfactual samples. Furthermore we propose a C-REC framework. We first adopt three encoders to extract image text and attribute features. Then our dual-branch attentive fusion module fuses these cross-modal features with two branches by an attention mechanism. At last two prediction heads generate a bounding box and a counterfactual label respectively. In addition we incorporate contrastive learning with the generated counterfactual samples as negatives to enhance the counterfactual perception. Extensive experiments show that our framework achieves promising performance on both public REC datasets RefCOCO/+/g and our constructed C-REC datasets C-RefCOCO/+/g. The code and data are available at https://github.com/Glacier0012/CREC. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Revisiting_Counterfactual_Problems_in_Referring_Expression_Comprehension_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Revisiting_Counterfactual_Problems_in_Referring_Expression_Comprehension_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Revisiting_Counterfactual_Problems_in_Referring_Expression_Comprehension_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_Revisiting_Counterfactual_Problems_CVPR_2024_supplemental.pdf | null |
DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video | Huiqiang Sun, Xingyi Li, Liao Shen, Xinyi Ye, Ke Xian, Zhiguo Cao | Recent advancements in dynamic neural radiance field methods have yielded remarkable outcomes. However these approaches rely on the assumption of sharp input images. When faced with motion blur existing dynamic NeRF methods often struggle to generate high-quality novel views. In this paper we propose DyBluRF a dynamic radiance field approach that synthesizes sharp novel views from a monocular video affected by motion blur. To account for motion blur in input images we simultaneously capture the camera trajectory and object Discrete Cosine Transform (DCT) trajectories within the scene. Additionally we employ a global cross-time rendering approach to ensure consistent temporal coherence across the entire scene. We curate a dataset comprising diverse dynamic scenes that are specifically tailored for our task. Experimental results on our dataset demonstrate that our method outperforms existing approaches in generating sharp novel views from motion-blurred inputs while maintaining spatial-temporal consistency of the scene. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_DyBluRF_Dynamic_Neural_Radiance_Fields_from_Blurry_Monocular_Video_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10103 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_DyBluRF_Dynamic_Neural_Radiance_Fields_from_Blurry_Monocular_Video_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_DyBluRF_Dynamic_Neural_Radiance_Fields_from_Blurry_Monocular_Video_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_DyBluRF_Dynamic_Neural_CVPR_2024_supplemental.pdf | null |
Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis | Simon Niedermayr, Josef Stumpfegger, Rüdiger Westermann | Recently high-fidelity scene reconstruction with an optimized 3D Gaussian splat representation has been introduced for novel view synthesis from sparse image sets. Making such representations suitable for applications like network streaming and rendering on low-power devices requires significantly reduced memory consumption as well as improved rendering efficiency. We propose a compressed 3D Gaussian splat representation that utilizes sensitivity-aware vector clustering with quantization-aware training to compress directional colors and Gaussian parameters. The learned codebooks have low bitrates and achieve a compression rate of up to 31 on real-world scenes with only minimal degradation of visual quality. We demonstrate that the compressed splat representation can be efficiently rendered with hardware rasterization on lightweight GPUs at up to 4 higher framerates than reported via an optimized GPU compute pipeline. Extensive experiments across multiple datasets demonstrate the robustness and rendering speed of the proposed approach. | https://openaccess.thecvf.com/content/CVPR2024/papers/Niedermayr_Compressed_3D_Gaussian_Splatting_for_Accelerated_Novel_View_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.02436 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Niedermayr_Compressed_3D_Gaussian_Splatting_for_Accelerated_Novel_View_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Niedermayr_Compressed_3D_Gaussian_Splatting_for_Accelerated_Novel_View_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Niedermayr_Compressed_3D_Gaussian_CVPR_2024_supplemental.pdf | null |
Separating the "Chirp" from the "Chat": Self-supervised Visual Grounding of Sound and Language | Mark Hamilton, Andrew Zisserman, John R. Hershey, William T. Freeman | We present DenseAV a novel dual encoder grounding architecture that learns high-resolution semantically meaningful and audio-visual aligned features solely through watching videos. We show that DenseAV can discover the "meaning" of words and the "location" of sounds without explicit localization supervision. Furthermore it automatically discovers and distinguishes between these two types of associations without supervision. We show that DenseAV's localization abilities arise from a new multi-head feature aggregation operator that directly compares dense image and audio representations for contrastive learning. In contrast many other systems that learn "global" audio and video representations cannot localize words and sound. Finally we contribute two new datasets to improve the evaluation of AV representations through speech and sound prompted semantic segmentation. On these and other datasets we show DenseAV dramatically outperforms the prior art on speech and sound prompted semantic segmentation. DenseAV outperforms the current state-of-the-art ImageBind on cross-modal retrieval using fewer than half of the parameters. Project Page: https://aka.ms/denseav | https://openaccess.thecvf.com/content/CVPR2024/papers/Hamilton_Separating_the_Chirp_from_the_Chat_Self-supervised_Visual_Grounding_of_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hamilton_Separating_the_Chirp_from_the_Chat_Self-supervised_Visual_Grounding_of_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hamilton_Separating_the_Chirp_from_the_Chat_Self-supervised_Visual_Grounding_of_CVPR_2024_paper.html | CVPR 2024 | null | null |
Towards Generalizing to Unseen Domains with Few Labels | Chamuditha Jayanga Galappaththige, Sanoojan Baliah, Malitha Gunawardhana, Muhammad Haris Khan | We approach the challenge of addressing semi-supervised domain generalization (SSDG). Specifically our aim is to obtain a model that learns domain-generalizable features by leveraging a limited subset of labelled data alongside a substantially larger pool of unlabeled data. Existing domain generalization (DG) methods which are unable to exploit unlabeled data perform poorly compared to semi-supervised learning (SSL) methods under SSDG setting. Nevertheless SSL methods have considerable room for performance improvement when compared to fully-supervised DG training. To tackle this underexplored yet highly practical problem of SSDG we make the following core contributions. First we propose a feature-based conformity technique that matches the posterior distributions from the feature space with the pseudo-label from the model's output space. Second we develop a semantics alignment loss to learn semantically-compatible representations by regularizing the semantic structure in the feature space. Our method is plug-and-play and can be readily integrated with different SSL-based SSDG baselines without introducing any additional parameters. Extensive experimental results across five challenging DG benchmarks with four strong SSL baselines suggest that our method provides consistent and notable gains in two different SSDG settings. | https://openaccess.thecvf.com/content/CVPR2024/papers/Galappaththige_Towards_Generalizing_to_Unseen_Domains_with_Few_Labels_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Galappaththige_Towards_Generalizing_to_Unseen_Domains_with_Few_Labels_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Galappaththige_Towards_Generalizing_to_Unseen_Domains_with_Few_Labels_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Galappaththige_Towards_Generalizing_to_CVPR_2024_supplemental.pdf | null |
MA-LMM: Memory-Augmented Large Multimodal Model for Long-Term Video Understanding | Bo He, Hengduo Li, Young Kyun Jang, Menglin Jia, Xuefei Cao, Ashish Shah, Abhinav Shrivastava, Ser-Nam Lim | With the success of large language models (LLMs) integrating the vision model into LLMs to build vision-language foundation models has gained much more interest recently. However existing LLM-based large multimodal models (e.g. Video-LLaMA VideoChat) can only take in a limited number of frames for short video understanding. In this study we mainly focus on designing an efficient and effective model for long-term video understanding. Instead of trying to process more frames simultaneously like most existing work we propose to process videos in an online manner and store past video information in a memory bank. This allows our model to reference historical video content for long-term analysis without exceeding LLMs' context length constraints or GPU memory limits. Our memory bank can be seamlessly integrated into current multimodal LLMs in an off-the-shelf manner. We conduct extensive experiments on various video understanding tasks such as long-video understanding video question answering and video captioning and our model can achieve state-of-the-art performances across multiple datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/He_MA-LMM_Memory-Augmented_Large_Multimodal_Model_for_Long-Term_Video_Understanding_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/He_MA-LMM_Memory-Augmented_Large_Multimodal_Model_for_Long-Term_Video_Understanding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/He_MA-LMM_Memory-Augmented_Large_Multimodal_Model_for_Long-Term_Video_Understanding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/He_MA-LMM_Memory-Augmented_Large_CVPR_2024_supplemental.pdf | null |
AAMDM: Accelerated Auto-regressive Motion Diffusion Model | Tianyu Li, Calvin Qiao, Guanqiao Ren, KangKang Yin, Sehoon Ha | Interactive motion synthesis is essential in creating immersive experiences in entertainment applications such as video games and virtual reality. However generating animations that are both high-quality and contextually responsive remains a challenge. Traditional techniques in the game industry can produce high-fidelity animations but suffer from high computational costs and poor scalability. Trained neural network models alleviate the memory and speed issues yet fall short on generating diverse motions. Diffusion models offer diverse motion synthesis with low memory usage but require expensive reverse diffusion processes. This paper introduces the Accelerated Auto-regressive Motion Diffusion Model (AAMDM) a novel motion synthesis framework designed to achieve quality diversity and efficiency all together. AAMDM integrates Denoising Diffusion GANs as a fast Generation Module and an Auto-regressive Diffusion Model as a Polishing Module. Furthermore AAMDM operates in a lower-dimensional embedded space rather than the full-dimensional pose space which reduces the training complexity as well as further improves the performance. We show that AAMDM outperforms existing methods in motion quality diversity and runtime efficiency through comprehensive quantitative analyses and visual comparisons. We also demonstrate the effectiveness of each algorithmic component through ablation studies. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_AAMDM_Accelerated_Auto-regressive_Motion_Diffusion_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.06146 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_AAMDM_Accelerated_Auto-regressive_Motion_Diffusion_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_AAMDM_Accelerated_Auto-regressive_Motion_Diffusion_Model_CVPR_2024_paper.html | CVPR 2024 | null | null |
Towards Understanding Cross and Self-Attention in Stable Diffusion for Text-Guided Image Editing | Bingyan Liu, Chengyu Wang, Tingfeng Cao, Kui Jia, Jun Huang | Deep Text-to-Image Synthesis (TIS) models such as Stable Diffusion have recently gained significant popularity for creative text-to-image generation. However for domain-specific scenarios tuning-free Text-guided Image Editing (TIE) is of greater importance for application developers. This approach modifies objects or object properties in images by manipulating feature components in attention layers during the generation process. Nevertheless little is known about the semantic meanings that these attention layers have learned and which parts of the attention maps contribute to the success of image editing. In this paper we conduct an in-depth probing analysis and demonstrate that cross-attention maps in Stable Diffusion often contain object attribution information which can result in editing failures. In contrast self-attention maps play a crucial role in preserving the geometric and shape details of the source image during the transformation to the target image. Our analysis offers valuable insights into understanding cross and self-attention mechanisms in diffusion models. Furthermore based on our findings we propose a simplified yet more stable and efficient tuning-free procedure that modifies only the self-attention maps of specified attention layers during the denoising process. Experimental results show that our simplified method consistently surpasses the performance of popular approaches on multiple datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Towards_Understanding_Cross_and_Self-Attention_in_Stable_Diffusion_for_Text-Guided_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.03431 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Towards_Understanding_Cross_and_Self-Attention_in_Stable_Diffusion_for_Text-Guided_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Towards_Understanding_Cross_and_Self-Attention_in_Stable_Diffusion_for_Text-Guided_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Towards_Understanding_Cross_CVPR_2024_supplemental.pdf | null |
Dr2Net: Dynamic Reversible Dual-Residual Networks for Memory-Efficient Finetuning | Chen Zhao, Shuming Liu, Karttikeya Mangalam, Guocheng Qian, Fatimah Zohra, Abdulmohsen Alghannam, Jitendra Malik, Bernard Ghanem | Large pretrained models are increasingly crucial in modern computer vision tasks. These models are typically used in downstream tasks by end-to-end finetuning which is highly memory-intensive for tasks with high-resolution data e.g. video understanding small object detection and point cloud analysis. In this paper we propose Dynamic Reversible Dual-Residual Networks or Dr2Net a novel family of network architectures that acts as a surrogate network to finetune a pretrained model with substantially reduced memory consumption. Dr2Net contains two types of residual connections one maintaining the residual structure in the pretrained models and the other making the network reversible. Due to its reversibility intermediate activations which can be reconstructed from output are cleared from memory during training. We use two coefficients on either type of residual connections respectively and introduce a dynamic training strategy that seamlessly transitions the pretrained model to a reversible network with much higher numerical precision. We evaluate Dr2Net on various pretrained models and various tasks and show that it can reach comparable performance to conventional finetuning but with significantly less memory usage. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_Dr2Net_Dynamic_Reversible_Dual-Residual_Networks_for_Memory-Efficient_Finetuning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Dr2Net_Dynamic_Reversible_Dual-Residual_Networks_for_Memory-Efficient_Finetuning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Dr2Net_Dynamic_Reversible_Dual-Residual_Networks_for_Memory-Efficient_Finetuning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Dr2Net_Dynamic_Reversible_CVPR_2024_supplemental.pdf | null |
PNeRV: Enhancing Spatial Consistency via Pyramidal Neural Representation for Videos | Qi Zhao, M. Salman Asif, Zhan Ma | The primary focus of Neural Representation for Videos (NeRV) is to effectively model its spatiotemporal consistency. However current NeRV systems often face a significant issue of spatial inconsistency leading to decreased perceptual quality. To address this issue we introduce the Pyramidal Neural Representation for Videos (PNeRV) which is built on a multi-scale information connection and comprises a lightweight rescaling operator Kronecker Fully-connected layer (KFc) and a Benign Selective Memory (BSM) mechanism. The KFc inspired by the tensor decomposition of the vanilla Fully-connected layer facilitates low-cost rescaling and global correlation modeling. BSM merges high-level features with granular ones adaptively. Furthermore we provide an analysis based on the Universal Approximation Theory of the NeRV system and validate the effectiveness of the proposed PNeRV. We conducted comprehensive experiments to demonstrate that PNeRV surpasses the performance of contemporary NeRV models achieving the best results in video regression on UVG and DAVIS under various metrics (PSNR SSIM LPIPS and FVD). Compared to vanilla NeRV PNeRV achieves a +4.49 dB gain in PSNR and a 231% increase in FVD on UVG along with a +3.28 dB PSNR and 634% FVD increase on DAVIS. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_PNeRV_Enhancing_Spatial_Consistency_via_Pyramidal_Neural_Representation_for_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.08921 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_PNeRV_Enhancing_Spatial_Consistency_via_Pyramidal_Neural_Representation_for_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_PNeRV_Enhancing_Spatial_Consistency_via_Pyramidal_Neural_Representation_for_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_PNeRV_Enhancing_Spatial_CVPR_2024_supplemental.pdf | null |
LTGC: Long-tail Recognition via Leveraging LLMs-driven Generated Content | Qihao Zhao, Yalun Dai, Hao Li, Wei Hu, Fan Zhang, Jun Liu | Long-tail recognition is challenging because it requires the model to learn good representations from tail categories and address imbalances across all categories. In this paper we propose a novel generative and fine-tuning framework LTGC to handle long-tail recognition via leveraging generated content. Firstly inspired by the rich implicit knowledge in large-scale models (e.g. large language models LLMs) LTGC leverages the power of these models to parse and reason over the original tail data to produce diverse tail-class content. We then propose several novel designs for LTGC to ensure the quality of the generated data and to efficiently fine-tune the model using both the generated and original data. The visualization demonstrates the effectiveness of the generation module in LTGC which produces accurate and diverse tail data. Additionally the experimental results demonstrate that our LTGC outperforms existing state-of-the-art methods on popular long-tailed benchmarks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_LTGC_Long-tail_Recognition_via_Leveraging_LLMs-driven_Generated_Content_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.05854 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_LTGC_Long-tail_Recognition_via_Leveraging_LLMs-driven_Generated_Content_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_LTGC_Long-tail_Recognition_via_Leveraging_LLMs-driven_Generated_Content_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_LTGC_Long-tail_Recognition_CVPR_2024_supplemental.pdf | null |
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative Data | Chengxiang Fan, Muzhi Zhu, Hao Chen, Yang Liu, Weijia Wu, Huaqi Zhang, Chunhua Shen | Instance segmentation is data-hungry and as model capacity increases data scale becomes crucial for improving the accuracy. Most instance segmentation datasets today require costly manual annotation limiting their data scale. Models trained on such data are prone to overfitting on the training set especially for those rare categories. While recent works have delved into exploiting generative models to create synthetic datasets for data augmentation these approaches do not efficiently harness the full potential of generative models. To address these issues we introduce a more efficient strategy to construct generative datasets for data augmentation termed DiverGen. Firstly we provide an explanation of the role of generative data from the perspective of distribution discrepancy. We investigate the impact of different data on the distribution learned by the model. We argue that generative data can expand the data distribution that the model can learn thus mitigating overfitting. Additionally we find that the diversity of generative data is crucial for improving model performance and enhance it through various strategies including category diversity prompt diversity and generative model diversity. With these strategies we can scale the data to millions while maintaining the trend of model performance improvement. On the LVIS dataset DiverGen significantly outperforms the strong model X-Paste achieving +1.1 box AP and +1.1 mask AP across all categories and +1.9 box AP and +2.5 mask AP for rare categories. Our codes are available at https://github.com/aim-uofa/DiverGen. | https://openaccess.thecvf.com/content/CVPR2024/papers/Fan_DiverGen_Improving_Instance_Segmentation_by_Learning_Wider_Data_Distribution_with_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.10185 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_DiverGen_Improving_Instance_Segmentation_by_Learning_Wider_Data_Distribution_with_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_DiverGen_Improving_Instance_Segmentation_by_Learning_Wider_Data_Distribution_with_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Fan_DiverGen_Improving_Instance_CVPR_2024_supplemental.pdf | null |
Neural Refinement for Absolute Pose Regression with Feature Synthesis | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Neural_Refinement_for_Absolute_Pose_Regression_with_Feature_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Neural_Refinement_for_Absolute_Pose_Regression_with_Feature_Synthesis_CVPR_2024_paper.html | CVPR 2024 | null | null |
Learning Disentangled Identifiers for Action-Customized Text-to-Image Generation | Siteng Huang, Biao Gong, Yutong Feng, Xi Chen, Yuqian Fu, Yu Liu, Donglin Wang | This study focuses on a novel task in text-to-image (T2I) generation namely action customization. The objective of this task is to learn the co-existing action from limited data and generalize it to unseen humans or even animals. Experimental results show that existing subject-driven customization methods fail to learn the representative characteristics of actions and struggle in decoupling actions from context features including appearance. To overcome the preference for low-level features and the entanglement of high-level features we propose an inversion-based method Action-Disentangled Identifier (ADI) to learn action-specific identifiers from the exemplar images. ADI first expands the semantic conditioning space by introducing layer-wise identifier tokens thereby increasing the representational richness while distributing the inversion across different features. Then to block the inversion of action-agnostic features ADI extracts the gradient invariance from the constructed sample triples and masks the updates of irrelevant channels. To comprehensively evaluate the task we present an ActionBench that includes a variety of actions each accompanied by meticulously selected samples. Both quantitative and qualitative results show that our ADI outperforms existing baselines in action-customized T2I generation. Our project page is at https://adi-t2i.github.io/ADI. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_Learning_Disentangled_Identifiers_for_Action-Customized_Text-to-Image_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.15841 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_Learning_Disentangled_Identifiers_for_Action-Customized_Text-to-Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_Learning_Disentangled_Identifiers_for_Action-Customized_Text-to-Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_Learning_Disentangled_Identifiers_CVPR_2024_supplemental.pdf | null |
Automatic Controllable Colorization via Imagination | Xiaoyan Cong, Yue Wu, Qifeng Chen, Chenyang Lei | We propose a framework for automatic colorization that allows for iterative editing and modifications. The core of our framework lies in an imagination module: by understanding the content within a grayscale image we utilize a pre-trained image generation model to generate multiple images that contain the same content. These images serve as references for coloring mimicking the process of human experts. As the synthesized images can be imperfect or different from the original grayscale image we propose a Reference Refinement Module to select the optimal reference composition. Unlike most previous end-to-end automatic colorization algorithms our framework allows for iterative and localized modifications of the colorization results because we explicitly model the coloring samples. Extensive experiments demonstrate the superiority of our framework over existing automatic colorization algorithms in editability and flexibility. Project page: https://xy-cong.github.io/imagine-colorization/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cong_Automatic_Controllable_Colorization_via_Imagination_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.05661 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cong_Automatic_Controllable_Colorization_via_Imagination_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cong_Automatic_Controllable_Colorization_via_Imagination_CVPR_2024_paper.html | CVPR 2024 | null | null |
Point Transformer V3: Simpler Faster Stronger | Xiaoyang Wu, Li Jiang, Peng-Shuai Wang, Zhijian Liu, Xihui Liu, Yu Qiao, Wanli Ouyang, Tong He, Hengshuang Zhao | This paper is not motivated to seek innovation within the attention mechanism. Instead it focuses on overcoming the existing trade-offs between accuracy and efficiency within the context of point cloud processing leveraging the power of scale. Drawing inspiration from recent advances in 3D large-scale representation learning we recognize that model performance is more influenced by scale than by intricate design. Therefore we present Point Transformer V3 (PTv3) which prioritizes simplicity and efficiency over the accuracy of certain mechanisms that are minor to the overall performance after scaling such as replacing the precise neighbor search by KNN with an efficient serialized neighbor mapping of point clouds organized with specific patterns. This principle enables significant scaling expanding the receptive field from 16 to 1024 points while remaining efficient (a 3x increase in processing speed and a 10x improvement in memory efficiency compared with its predecessor PTv2). PTv3 attains state-of-the-art results on over 20 downstream tasks that span both indoor and outdoor scenarios. Further enhanced with multi-dataset joint training PTv3 pushes these results to a higher level. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Point_Transformer_V3_Simpler_Faster_Stronger_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.10035 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Point_Transformer_V3_Simpler_Faster_Stronger_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Point_Transformer_V3_Simpler_Faster_Stronger_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_Point_Transformer_V3_CVPR_2024_supplemental.pdf | null |
DiffCast: A Unified Framework via Residual Diffusion for Precipitation Nowcasting | Demin Yu, Xutao Li, Yunming Ye, Baoquan Zhang, Chuyao Luo, Kuai Dai, Rui Wang, Xunlai Chen | Precipitation nowcasting is an important spatio-temporal prediction task to predict the radar echoes sequences based on current observations which can serve both meteorological science and smart city applications. Due to the chaotic evolution nature of the precipitation systems it is a very challenging problem. Previous studies address the problem either from the perspectives of deterministic modeling or probabilistic modeling. However their predictions suffer from the blurry high-value echoes fading away and position inaccurate issues. The root reason of these issues is that the chaotic evolutionary precipitation systems are not appropriately modeled. Inspired by the nature of the systems we propose to decompose and model them from the perspective of global deterministic motion and local stochastic variations with residual mechanism. A unified and flexible framework that can equip any type of spatio-temporal models is proposed based on residual diffusion which effectively tackles the shortcomings of previous methods. Extensive experimental results on four publicly available radar datasets demonstrate the effectiveness and superiority of the proposed framework compared to state-of-the-art techniques. Our code is publicly available at https://github.com/DeminYu98/DiffCast. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_DiffCast_A_Unified_Framework_via_Residual_Diffusion_for_Precipitation_Nowcasting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.06734 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_DiffCast_A_Unified_Framework_via_Residual_Diffusion_for_Precipitation_Nowcasting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_DiffCast_A_Unified_Framework_via_Residual_Diffusion_for_Precipitation_Nowcasting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_DiffCast_A_Unified_CVPR_2024_supplemental.pdf | null |
Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives | Kristen Grauman, Andrew Westbury, Lorenzo Torresani, Kris Kitani, Jitendra Malik, Triantafyllos Afouras, Kumar Ashutosh, Vijay Baiyya, Siddhant Bansal, Bikram Boote, Eugene Byrne, Zach Chavis, Joya Chen, Feng Cheng, Fu-Jen Chu, Sean Crane, Avijit Dasgupta, Jing Dong, Maria Escobar, Cristhian Forigua, Abrham Gebreselasie, Sanjay Haresh, Jing Huang, Md Mohaiminul Islam, Suyog Jain, Rawal Khirodkar, Devansh Kukreja, Kevin J Liang, Jia-Wei Liu, Sagnik Majumder, Yongsen Mao, Miguel Martin, Effrosyni Mavroudi, Tushar Nagarajan, Francesco Ragusa, Santhosh Kumar Ramakrishnan, Luigi Seminara, Arjun Somayazulu, Yale Song, Shan Su, Zihui Xue, Edward Zhang, Jinxu Zhang, Angela Castillo, Changan Chen, Xinzhu Fu, Ryosuke Furuta, Cristina Gonzalez, Prince Gupta, Jiabo Hu, Yifei Huang, Yiming Huang, Weslie Khoo, Anush Kumar, Robert Kuo, Sach Lakhavani, Miao Liu, Mi Luo, Zhengyi Luo, Brighid Meredith, Austin Miller, Oluwatumininu Oguntola, Xiaqing Pan, Penny Peng, Shraman Pramanick, Merey Ramazanova, Fiona Ryan, Wei Shan, Kiran Somasundaram, Chenan Song, Audrey Southerland, Masatoshi Tateno, Huiyu Wang, Yuchen Wang, Takuma Yagi, Mingfei Yan, Xitong Yang, Zecheng Yu, Shengxin Cindy Zha, Chen Zhao, Ziwei Zhao, Zhifan Zhu, Jeff Zhuo, Pablo Arbelaez, Gedas Bertasius, Dima Damen, Jakob Engel, Giovanni Maria Farinella, Antonino Furnari, Bernard Ghanem, Judy Hoffman, C.V. Jawahar, Richard Newcombe, Hyun Soo Park, James M. Rehg, Yoichi Sato, Manolis Savva, Jianbo Shi, Mike Zheng Shou, Michael Wray | We present Ego-Exo4D a diverse large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g. sports music dance bike repair). 740 participants from 13 cities worldwide performed these activities in 123 different natural scene contexts yielding long-form captures from 1 to 42 minutes each and 1286 hours of video combined. The multimodal nature of the dataset is unprecedented: the video is accompanied by multichannel audio eye gaze 3D point clouds camera poses IMU and multiple paired language descriptions---including a novel "expert commentary" done by coaches and teachers and tailored to the skilled-activity domain. To push the frontier of first-person video understanding of skilled human activity we also present a suite of benchmark tasks and their annotations including fine-grained activity understanding proficiency estimation cross-view translation and 3D hand/body pose. All resources are open sourced to fuel new research in the community. | https://openaccess.thecvf.com/content/CVPR2024/papers/Grauman_Ego-Exo4D_Understanding_Skilled_Human_Activity_from_First-_and_Third-Person_Perspectives_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Grauman_Ego-Exo4D_Understanding_Skilled_Human_Activity_from_First-_and_Third-Person_Perspectives_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Grauman_Ego-Exo4D_Understanding_Skilled_Human_Activity_from_First-_and_Third-Person_Perspectives_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Grauman_Ego-Exo4D_Understanding_Skilled_CVPR_2024_supplemental.pdf | null |
Point Cloud Pre-training with Diffusion Models | Xiao Zheng, Xiaoshui Huang, Guofeng Mei, Yuenan Hou, Zhaoyang Lyu, Bo Dai, Wanli Ouyang, Yongshun Gong | Pre-training a model and then fine-tuning it on downstream tasks has demonstrated significant success in the 2D image and NLP domains. However due to the unordered and non-uniform density characteristics of point clouds it is non-trivial to explore the prior knowledge of point clouds and pre-train a point cloud backbone. In this paper we propose a novel pre-training method called Point cloud Diffusion pre-training PointDif. We consider the point cloud pre-training task as a conditional point-to-point generation problem and introduce a conditional point generator. This generator aggregates the features extracted by the backbone and employs them as the condition to guide the point-to-point recovery from the noisy point cloud thereby assisting the backbone in capturing both local and global geometric priors as well as the global point density distribution of the object. We also present a recurrent uniform sampling optimization strategy which enables the model to uniformly recover from various noise levels and learn from balanced supervision. Our PointDif achieves substantial improvement across various real-world datasets for diverse downstream tasks such as classification segmentation and detection. Specifically PointDif attains 70.0% mIoU on S3DIS Area 5 for the segmentation task and achieves an average improvement of 2.4% on ScanObjectNN for the classification task compared to TAP. Furthermore our pre-training framework can be flexibly applied to diverse point cloud backbones and bring considerable gains. Code is available at https://github.com/zhengxiaozx/PointDif | https://openaccess.thecvf.com/content/CVPR2024/papers/Zheng_Point_Cloud_Pre-training_with_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.14960 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Point_Cloud_Pre-training_with_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Point_Cloud_Pre-training_with_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zheng_Point_Cloud_Pre-training_CVPR_2024_supplemental.pdf | null |
Mask4Align: Aligned Entity Prompting with Color Masks for Multi-Entity Localization Problems | Haoquan Zhang, Ronggang Huang, Yi Xie, Huaidong Zhang | In Visual Question Answering (VQA) recognizing and localizing entities pose significant challenges. Pretrained vision-and-language models have addressed this problem by providing a text description as the answer. However in visual scenes with multiple entities textual descriptions struggle to distinguish the entities from the same category effectively. Consequently the VQA dataset is limited by the limitations of text description and cannot adequately cover scenarios involving multiple entities. To address this challenge we introduce a Mask for Align (Mask4Align) method which can determine the entity's position in the given image that best matches the user-input question. This method incorporates colored masks into the image enabling the VQA model to handle discrimination and localization challenges associated with multiple entities. To process an arbitrary number of similar entities Mask4Align is designed hierarchically to discern subtle differences achieving precise localization. Since Mask4Align directly utilizes pre-trained models it does not introduce additional training overhead. Extensive experiments conducted on both the gaze target prediction task dataset and our proposed multi-entity localization dataset showcase the superiority of Mask4Align. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Mask4Align_Aligned_Entity_Prompting_with_Color_Masks_for_Multi-Entity_Localization_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Mask4Align_Aligned_Entity_Prompting_with_Color_Masks_for_Multi-Entity_Localization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Mask4Align_Aligned_Entity_Prompting_with_Color_Masks_for_Multi-Entity_Localization_CVPR_2024_paper.html | CVPR 2024 | null | null |
RCL: Reliable Continual Learning for Unified Failure Detection | Fei Zhu, Zhen Cheng, Xu-Yao Zhang, Cheng-Lin Liu, Zhaoxiang Zhang | Deep neural networks are known to be overconfident for what they don't know in the wild which is undesirable for decision-making in high-stakes applications. Despite quantities of existing works most of them focus on detecting out-of-distribution (OOD) samples from unseen classes while ignoring large parts of relevant failure sources like misclassified samples from known classes. In particular recent studies reveal that prevalent OOD detection methods are actually harmful for misclassification detection (MisD) indicating that there seems to be a tradeoff between those two tasks. In this paper we study the critical yet under-explored problem of unified failure detection which aims to detect both misclassified and OOD examples. Concretely we identify the failure of simply integrating learning objectives of misclassification and OOD detection and show the potential of sequence learning. Inspired by this we propose a reliable continual learning paradigm whose spirit is to equip the model with MisD ability first and then improve the OOD detection ability without degrading the already adequate MisD performance. Extensive experiments demonstrate that our method achieves strong unified failure detection performance. The code is available at https://github.com/Impression2805/RCL. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_RCL_Reliable_Continual_Learning_for_Unified_Failure_Detection_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_RCL_Reliable_Continual_Learning_for_Unified_Failure_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_RCL_Reliable_Continual_Learning_for_Unified_Failure_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_RCL_Reliable_Continual_CVPR_2024_supplemental.pdf | null |
Referring Image Editing: Object-level Image Editing via Referring Expressions | Chang Liu, Xiangtai Li, Henghui Ding | Significant advancements have been made in image editing with the recent advance of the Diffusion model. However most of the current methods primarily focus on global or subject-level modifications and often face limitations when it comes to editing specific objects when there are other objects coexisting in the scene given solely textual prompts. In response to this challenge we introduce an object-level generative task called Referring Image Editing (RIE) which enables the identification and editing of specific source objects in an image using text prompts. To tackle this task effectively we propose a tailored framework called ReferDiffusion. It aims to disentangle input prompts into multiple embeddings and employs a mixed-supervised multi-stage training strategy. To facilitate further research in this domain we introduce the RefCOCO-Edit dataset comprising images editing prompts source object segmentation masks and reference edited images for training and evaluation. Our extensive experiments demonstrate the effectiveness of our approach in identifying and editing target objects while conventional general image editing and region-based image editing methods have difficulties in this challenging task. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Referring_Image_Editing_Object-level_Image_Editing_via_Referring_Expressions_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Referring_Image_Editing_Object-level_Image_Editing_via_Referring_Expressions_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Referring_Image_Editing_Object-level_Image_Editing_via_Referring_Expressions_CVPR_2024_paper.html | CVPR 2024 | null | null |
CAMixerSR: Only Details Need More "Attention" | Yan Wang, Yi Liu, Shijie Zhao, Junlin Li, Li Zhang | To satisfy the rapidly increasing demands on the large image (2K-8K) super-resolution (SR) prevailing methods follow two independent tracks: 1) accelerate existing networks by content-aware routing and 2) design better super-resolution networks via token mixer refining. Despite directness they encounter unavoidable defects (e.g. inflexible route or non-discriminative processing) limiting further improvements of quality-complexity trade-off. To erase the drawbacks we integrate these schemes by proposing a content-aware mixer (CAMixer) which assigns convolution for simple contexts and additional deformable window-attention for sparse textures. Specifically the CAMixer uses a learnable predictor to generate multiple bootstraps including offsets for windows warping a mask for classifying windows and convolutional attentions for endowing convolution with the dynamic property which modulates attention to include more useful textures self-adaptively and improves the representation capability of convolution. We further introduce a global classification loss to improve the accuracy of predictors. By simply stacking CAMixers we obtain CAMixerSR which achieves superior performance on large-image SR lightweight SR and omnidirectional-image SR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_CAMixerSR_Only_Details_Need_More_Attention_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.19289 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_CAMixerSR_Only_Details_Need_More_Attention_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_CAMixerSR_Only_Details_Need_More_Attention_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_CAMixerSR_Only_Details_CVPR_2024_supplemental.pdf | null |
Towards Backward-Compatible Continual Learning of Image Compression | Zhihao Duan, Ming Lu, Justin Yang, Jiangpeng He, Zhan Ma, Fengqing Zhu | This paper explores the possibility of extending the capability of pre-trained neural image compressors (e.g. adapting to new data or target bitrates) without breaking backward compatibility the ability to decode bitstreams encoded by the original model. We refer to this problem as continual learning of image compression. Our initial findings show that baseline solutions such as end-to-end fine-tuning do not preserve the desired backward compatibility. To tackle this we propose a knowledge replay training strategy that effectively addresses this issue. We also design a new model architecture that enables more effective continual learning than existing baselines. Experiments are conducted for two scenarios: data-incremental learning and rate-incremental learning. The main conclusion of this paper is that neural image compressors can be fine-tuned to achieve better performance (compared to their pre-trained version) on new data and rates without compromising backward compatibility. The code is publicly available online. | https://openaccess.thecvf.com/content/CVPR2024/papers/Duan_Towards_Backward-Compatible_Continual_Learning_of_Image_Compression_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18862 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Duan_Towards_Backward-Compatible_Continual_Learning_of_Image_Compression_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Duan_Towards_Backward-Compatible_Continual_Learning_of_Image_Compression_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Duan_Towards_Backward-Compatible_Continual_CVPR_2024_supplemental.pdf | null |
Latent Modulated Function for Computational Optimal Continuous Image Representation | Zongyao He, Zhi Jin | The recent work Local Implicit Image Function (LIIF) and subsequent Implicit Neural Representation (INR) based works have achieved remarkable success in Arbitrary-Scale Super-Resolution (ASSR) by using MLP to decode Low-Resolution (LR) features. However these continuous image representations typically implement decoding in High-Resolution (HR) High-Dimensional (HD) space leading to a quadratic increase in computational cost and seriously hindering the practical applications of ASSR. To tackle this problem we propose a novel Latent Modulated Function (LMF) which decouples the HR-HD decoding process into shared latent decoding in LR-HD space and independent rendering in HR Low-Dimensional (LD) space thereby realizing the first computational optimal paradigm of continuous image representation. Specifically LMF utilizes an HD MLP in latent space to generate latent modulations of each LR feature vector. This enables a modulated LD MLP in render space to quickly adapt to any input feature vector and perform rendering at arbitrary resolution. Furthermore we leverage the positive correlation between modulation intensity and input image complexity to design a Controllable Multi-Scale Rendering (CMSR) algorithm offering the flexibility to adjust the decoding efficiency based on the rendering precision. Extensive experiments demonstrate that converting existing INR-based ASSR methods to LMF can reduce the computational cost by up to 99.9% accelerate inference by up to 57x and save up to 76% of parameters while maintaining competitive performance. The code is available at https://github.com/HeZongyao/LMF. | https://openaccess.thecvf.com/content/CVPR2024/papers/He_Latent_Modulated_Function_for_Computational_Optimal_Continuous_Image_Representation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.16451 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/He_Latent_Modulated_Function_for_Computational_Optimal_Continuous_Image_Representation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/He_Latent_Modulated_Function_for_Computational_Optimal_Continuous_Image_Representation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/He_Latent_Modulated_Function_CVPR_2024_supplemental.pdf | null |
Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training | Arun Reddy, William Paul, Corban Rivera, Ketul Shah, Celso M. de Melo, Rama Chellappa | In this work we tackle the problem of unsupervised domain adaptation (UDA) for video action recognition. Our approach which we call UNITE uses an image teacher model to adapt a video student model to the target domain. UNITE first employs self-supervised pre-training to promote discriminative feature learning on target domain videos using a teacher-guided masked distillation objective. We then perform self-training on masked target data using the video student model and image teacher model together to generate improved pseudolabels for unlabeled target videos. Our self-training process successfully leverages the strengths of both models to achieve strong transfer performance across domains. We evaluate our approach on multiple video domain adaptation benchmarks and observe significant improvements upon previously reported results. | https://openaccess.thecvf.com/content/CVPR2024/papers/Reddy_Unsupervised_Video_Domain_Adaptation_with_Masked_Pre-Training_and_Collaborative_Self-Training_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02914 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Reddy_Unsupervised_Video_Domain_Adaptation_with_Masked_Pre-Training_and_Collaborative_Self-Training_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Reddy_Unsupervised_Video_Domain_Adaptation_with_Masked_Pre-Training_and_Collaborative_Self-Training_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Reddy_Unsupervised_Video_Domain_CVPR_2024_supplemental.pdf | null |
UniDepth: Universal Monocular Metric Depth Estimation | Luigi Piccinelli, Yung-Hsu Yang, Christos Sakaridis, Mattia Segu, Siyuan Li, Luc Van Gool, Fisher Yu | Accurate monocular metric depth estimation (MMDE) is crucial to solving downstream tasks in 3D perception and modeling. However the remarkable accuracy of recent MMDE methods is confined to their training domains. These methods fail to generalize to unseen domains even in the presence of moderate domain gaps which hinders their practical applicability. We propose a new model UniDepth capable of reconstructing metric 3D scenes from solely single images across domains. Departing from the existing MMDE methods UniDepth directly predicts metric 3D points from the input image at inference time without any additional information striving for a universal and flexible MMDE solution. In particular UniDepth implements a self-promptable camera module predicting dense camera representation to condition depth features. Our model exploits a pseudo-spherical output representation which disentangles camera and depth representations. In addition we propose a geometric invariance loss that promotes the invariance of camera-prompted depth features. Thorough evaluations on ten datasets in a zero-shot regime consistently demonstrate the superior performance of UniDepth even when compared with methods directly trained on the testing domains. Code and models are available at: github.com/lpiccinelli-eth/unidepth | https://openaccess.thecvf.com/content/CVPR2024/papers/Piccinelli_UniDepth_Universal_Monocular_Metric_Depth_Estimation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18913 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Piccinelli_UniDepth_Universal_Monocular_Metric_Depth_Estimation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Piccinelli_UniDepth_Universal_Monocular_Metric_Depth_Estimation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Piccinelli_UniDepth_Universal_Monocular_CVPR_2024_supplemental.pdf | null |
EMOPortraits: Emotion-enhanced Multimodal One-shot Head Avatars | Nikita Drobyshev, Antoni Bigata Casademunt, Konstantinos Vougioukas, Zoe Landgraf, Stavros Petridis, Maja Pantic | Head avatars animated by visual signals have gained popularity particularly in cross-driving synthesis where the driver differs from the animated character a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model with a particular focus on its latent space for facial expression descriptors and uncover several limitations with its ability to express intense face motions. Head avatars animated by visual signals have gained popularity particularly in cross-driving synthesis where the driver differs from the animated character a challenging but highly practical approach. The recently presented MegaPortraits model has demonstrated state-of-the-art results in this domain. We conduct a deep examination and evaluation of this model with a particular focus on its latent space for facial expression descriptors and uncover several limitations with its ability to express intense face motions. To address these limitations we propose substantial changes in both training pipeline and model architecture to introduce our EMOPortraits model where we: Enhance the model's capability to faithfully support intense asymmetric face expressions setting a new state-of-the-art result in the emotion transfer task surpassing previous methods in both metrics and quality. Incorporate speech-driven mode to our model achieving top-tier performance in audio-driven facial animation making it possible to drive source identity through diverse modalities including visual signal audio or a blend of both.Furthermore we propose a novel multi-view video dataset featuring a wide range of intense and asymmetric facial expressions filling the gap with absence of such data in existing datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Drobyshev_EMOPortraits_Emotion-enhanced_Multimodal_One-shot_Head_Avatars_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.19110 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Drobyshev_EMOPortraits_Emotion-enhanced_Multimodal_One-shot_Head_Avatars_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Drobyshev_EMOPortraits_Emotion-enhanced_Multimodal_One-shot_Head_Avatars_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Drobyshev_EMOPortraits_Emotion-enhanced_Multimodal_CVPR_2024_supplemental.pdf | null |
NeuRAD: Neural Rendering for Autonomous Driving | Adam Tonderski, Carl Lindström, Georg Hess, William Ljungbergh, Lennart Svensson, Christoffer Petersson | Neural radiance fields (NeRFs) have gained popularity in the autonomous driving (AD) community. Recent methods show NeRFs' potential for closed-loop simulation enabling testing of AD systems and as an advanced training data augmentation technique. However existing methods often require long training times dense semantic supervision or lack generalizability. This in turn hinders the application of NeRFs for AD at scale. In this paper we propose \modelname a robust novel view synthesis method tailored to dynamic AD data. Our method features simple network design extensive sensor modeling for both camera and lidar -- including rolling shutter beam divergence and ray dropping -- and is applicable to multiple datasets out of the box. We verify its performance on five popular AD datasets achieving state-of-the-art performance across the board. To encourage further development we openly release the NeuRAD source code at https://github.com/georghess/NeuRAD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tonderski_NeuRAD_Neural_Rendering_for_Autonomous_Driving_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tonderski_NeuRAD_Neural_Rendering_for_Autonomous_Driving_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tonderski_NeuRAD_Neural_Rendering_for_Autonomous_Driving_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tonderski_NeuRAD_Neural_Rendering_CVPR_2024_supplemental.pdf | null |
VideoCutLER: Surprisingly Simple Unsupervised Video Instance Segmentation | Xudong Wang, Ishan Misra, Ziyun Zeng, Rohit Girdhar, Trevor Darrell | Existing approaches to unsupervised video instance segmentation typically rely on motion estimates and experience difficulties tracking small or divergent motions. We present VideoCutLER a simple method for unsupervised multi-instance video segmentation without using motion-based learning signals like optical flow or training on natural videos. Our key insight is that using high-quality pseudo masks and a simple video synthesis method for model training is surprisingly sufficient to enable the resulting video model to effectively segment and track multiple instances across video frames. We show the first competitive unsupervised learning results on the challenging YouTubeVIS-2019 benchmark achieving 50.7% AP50 surpassing the previous state-of-the-art by a large margin. VideoCutLER can also serve as a strong pretrained model for supervised video instance segmentation tasks exceeding DINO by 15.9% on YouTubeVIS-2019 in terms of AP. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_VideoCutLER_Surprisingly_Simple_Unsupervised_Video_Instance_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2308.14710 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_VideoCutLER_Surprisingly_Simple_Unsupervised_Video_Instance_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_VideoCutLER_Surprisingly_Simple_Unsupervised_Video_Instance_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_VideoCutLER_Surprisingly_Simple_CVPR_2024_supplemental.pdf | null |
Bootstrapping Chest CT Image Understanding by Distilling Knowledge from X-ray Expert Models | Weiwei Cao, Jianpeng Zhang, Yingda Xia, Tony C. W. Mok, Zi Li, Xianghua Ye, Le Lu, Jian Zheng, Yuxing Tang, Ling Zhang | Radiologists highly desire fully automated versatile AI for medical imaging interpretation. However the lack of extensively annotated large-scale multi-disease datasets has hindered the achievement of this goal. In this paper we explore the feasibility of leveraging language as a naturally high-quality supervision for chest CT imaging. In light of the limited availability of image-report pairs we bootstrap the understanding of 3D chest CT images by distilling chest-related diagnostic knowledge from an extensively pre-trained 2D X-ray expert model. Specifically we propose a language-guided retrieval method to match each 3D CT image with its semantically closest 2D X-ray image and perform pair-wise and semantic relation knowledge distillation. Subsequently we use contrastive learning to align images and reports within the same patient while distinguishing them from the other patients. However the challenge arises when patients have similar semantic diagnoses such as healthy patients potentially confusing if treated as negatives. We introduce a robust contrastive learning that identifies and corrects these false negatives. We train our model with over 12K pairs of chest CT images and radiology reports. Extensive experiments across multiple scenarios including zero-shot learning report generation and fine-tuning processes demonstrate the model's feasibility in interpreting chest CT images. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cao_Bootstrapping_Chest_CT_Image_Understanding_by_Distilling_Knowledge_from_X-ray_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_Bootstrapping_Chest_CT_Image_Understanding_by_Distilling_Knowledge_from_X-ray_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_Bootstrapping_Chest_CT_Image_Understanding_by_Distilling_Knowledge_from_X-ray_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cao_Bootstrapping_Chest_CT_CVPR_2024_supplemental.pdf | null |
Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification | Pingping Zhang, Yuhao Wang, Yang Liu, Zhengzheng Tu, Huchuan Lu | Single-modal object re-identification (ReID) faces great challenges in maintaining robustness within complex visual scenarios. In contrast multi-modal object ReID utilizes complementary information from diverse modalities showing great potentials for practical applications. However previous methods may be easily affected by irrelevant backgrounds and usually ignore the modality gaps. To address above issues we propose a novel learning framework named EDITOR to select diverse tokens from vision Transformers for multi-modal object ReID. We begin with a shared vision Transformer to extract tokenized features from different input modalities. Then we introduce a Spatial-Frequency Token Selection (SFTS) module to adaptively select object-centric tokens with both spatial and frequency information. Afterwards we employ a Hierarchical Masked Aggregation (HMA) module to facilitate feature interactions within and across modalities. Finally to further reduce the effect of backgrounds we propose a Background Consistency Constraint (BCC) and an Object-Centric Feature Refinement (OCFR). They are formulated as two new loss functions which improve the feature discrimination with background suppression. As a result our framework can generate more discriminative features for multi-modal object ReID. Extensive experiments on three multi-modal ReID benchmarks verify the effectiveness of our methods. The code is available at https://github.com/924973292/EDITOR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Magic_Tokens_Select_Diverse_Tokens_for_Multi-modal_Object_Re-Identification_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10254 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Magic_Tokens_Select_Diverse_Tokens_for_Multi-modal_Object_Re-Identification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Magic_Tokens_Select_Diverse_Tokens_for_Multi-modal_Object_Re-Identification_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Magic_Tokens_Select_CVPR_2024_supplemental.pdf | null |
Open3DIS: Open-Vocabulary 3D Instance Segmentation with 2D Mask Guidance | Phuc Nguyen, Tuan Duc Ngo, Evangelos Kalogerakis, Chuang Gan, Anh Tran, Cuong Pham, Khoi Nguyen | We introduce Open3DIS a novel solution designed to tackle the problem of Open-Vocabulary Instance Segmentation within 3D scenes. Objects within 3D environments exhibit diverse shapes scales and colors making precise instance-level identification a challenging task. Recent advancements in Open-Vocabulary scene understanding have made significant strides in this area by employing class-agnostic 3D instance proposal networks for object localization and learning queryable features for each 3D mask. While these methods produce high-quality instance proposals they struggle with identifying small-scale and geometrically ambiguous objects. The key idea of our method is a new module that aggregates 2D instance masks across frames and maps them to geometrically coherent point cloud regions as high-quality object proposals addressing the above limitations. These are then combined with 3D class-agnostic instance proposals to include a wide range of objects in the real world. To validate our approach we conducted experiments on three prominent datasets including ScanNet200 S3DIS and Replica demonstrating significant performance gains in segmenting objects with diverse categories over the state-of-the-art approaches. | https://openaccess.thecvf.com/content/CVPR2024/papers/Nguyen_Open3DIS_Open-Vocabulary_3D_Instance_Segmentation_with_2D_Mask_Guidance_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.10671 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Nguyen_Open3DIS_Open-Vocabulary_3D_Instance_Segmentation_with_2D_Mask_Guidance_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Nguyen_Open3DIS_Open-Vocabulary_3D_Instance_Segmentation_with_2D_Mask_Guidance_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Nguyen_Open3DIS_Open-Vocabulary_3D_CVPR_2024_supplemental.pdf | null |
SignGraph: A Sign Sequence is Worth Graphs of Nodes | Shiwei Gan, Yafeng Yin, Zhiwei Jiang, Hongkai Wen, Lei Xie, Sanglu Lu | Despite the recent success of sign language research the widely adopted CNN-based backbones are mainly migrated from other computer vision tasks in which the contours and texture of objects are crucial for identifying objects. They usually treat sign frames as grids and may fail to capture effective cross-region features. In fact sign language tasks need to focus on the correlation of different regions in one frame and the interaction of different regions among adjacent frames for identifying a sign sequence. In this paper we propose to represent a sign sequence as graphs and introduce a simple yet effective graph-based sign language processing architecture named SignGraph to extract cross-region features at the graph level. SignGraph consists of two basic modules: Local Sign Graph (LSG) module for learning the correlation of intra-frame cross-region features in one frame and Temporal Sign Graph (TSG) module for tracking the interaction of inter-frame cross-region features among adjacent frames. With LSG and TSG we build our model in a multiscale manner to ensure that the representation of nodes can capture cross-region features at different granularities. Extensive experiments on current public sign language datasets demonstrate the superiority of our SignGraph model. Our model achieves very competitive performances with the SOTA model while not using any extra cues. Code and models are available at: https://github.com/gswycf/SignGraph. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gan_SignGraph_A_Sign_Sequence_is_Worth_Graphs_of_Nodes_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gan_SignGraph_A_Sign_Sequence_is_Worth_Graphs_of_Nodes_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gan_SignGraph_A_Sign_Sequence_is_Worth_Graphs_of_Nodes_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gan_SignGraph_A_Sign_CVPR_2024_supplemental.pdf | null |
ControlRoom3D: Room Generation using Semantic Proxy Rooms | Jonas Schult, Sam Tsai, Lukas Höllein, Bichen Wu, Jialiang Wang, Chih-Yao Ma, Kunpeng Li, Xiaofang Wang, Felix Wimbauer, Zijian He, Peizhao Zhang, Bastian Leibe, Peter Vajda, Ji Hou | Manually creating 3D environments for AR/VR applications is a complex process requiring expert knowledge in 3D modeling software. Pioneering works facilitate this process by generating room meshes conditioned on textual style descriptions. Yet many of these automatically generated 3D meshes do not adhere to typical room layouts compromising their plausibility e.g. by placing several beds in one bedroom. To address these challenges we present ControlRoom3D a novel method to generate high-quality room meshes. Central to our approach is a user-defined 3D semantic proxy room that outlines a rough room layout based on semantic bounding boxes and a textual description of the overall room style. Our key insight is that when rendered to 2D this 3D representation provides valuable geometric and semantic information to control powerful 2D models to generate 3D consistent textures and geometry that aligns well with the proxy room. Backed up by an extensive study including quantitative metrics and qualitative user evaluations our method generates diverse and globally plausible 3D room meshes thus empowering users to design 3D rooms effortlessly without specialized knowledge. | https://openaccess.thecvf.com/content/CVPR2024/papers/Schult_ControlRoom3D_Room_Generation_using_Semantic_Proxy_Rooms_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Schult_ControlRoom3D_Room_Generation_using_Semantic_Proxy_Rooms_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Schult_ControlRoom3D_Room_Generation_using_Semantic_Proxy_Rooms_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Schult_ControlRoom3D_Room_Generation_CVPR_2024_supplemental.pdf | null |
DeconfuseTrack: Dealing with Confusion for Multi-Object Tracking | Cheng Huang, Shoudong Han, Mengyu He, Wenbo Zheng, Yuhao Wei | Accurate data association is crucial in reducing confusion such as ID switches and assignment errors in multi-object tracking (MOT). However existing advanced methods often overlook the diversity among trajectories and the ambiguity and conflicts present in motion and appearance cues leading to confusion among detections trajectories and associations when performing simple global data association. To address this issue we propose a simple versatile and highly interpretable data association approach called Decomposed Data Association (DDA). DDA decomposes the traditional association problem into multiple sub-problems using a series of non-learning-based modules and selectively addresses the confusion in each sub-problem by incorporating targeted exploitation of new cues. Additionally we introduce Occlusion-aware Non-Maximum Suppression (ONMS) to retain more occluded detections thereby increasing opportunities for association with trajectories and indirectly reducing the confusion caused by missed detections. Finally based on DDA and ONMS we design a powerful multi-object tracker named DeconfuseTrack specifically focused on resolving confusion in MOT. Extensive experiments conducted on the MOT17 and MOT20 datasets demonstrate that our proposed DDA and ONMS significantly enhance the performance of several popular trackers. Moreover DeconfuseTrack achieves state-of-the-art performance on the MOT17 and MOT20 test sets significantly outperforms the baseline tracker ByteTrack in metrics such as HOTA IDF1 AssA. This validates that our tracking design effectively reduces confusion caused by simple global association. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_DeconfuseTrack_Dealing_with_Confusion_for_Multi-Object_Tracking_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.02767 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_DeconfuseTrack_Dealing_with_Confusion_for_Multi-Object_Tracking_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_DeconfuseTrack_Dealing_with_Confusion_for_Multi-Object_Tracking_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_DeconfuseTrack_Dealing_with_CVPR_2024_supplemental.pdf | null |
PAPR in Motion: Seamless Point-level 3D Scene Interpolation | Shichong Peng, Yanshu Zhang, Ke Li | We propose the problem of point-level 3D scene interpolation which aims to simultaneously reconstruct a 3D scene in two states from multiple views synthesize smooth point-level interpolations between them and render the scene from novel viewpoints all without any supervision between the states. The primary challenge is on achieving a smooth transition between states that may involve significant and non-rigid changes. To address these challenges we introduce "PAPR in Motion" a novel approach that builds upon the recent Proximity Attention Point Rendering (PAPR) technique which can deform a point cloud to match a significantly different shape and render a visually coherent scene even after non-rigid deformations. Our approach is specifically designed to maintain the temporal consistency of the geometric structure by introducing various regularization techniques for PAPR. The result is a method that can effectively bridge large scene changes and produce visually coherent and temporally smooth interpolations in both geometry and appearance. Evaluation across diverse motion types demonstrates that "PAPR in Motion" outperforms the leading neural renderer for dynamic scenes. For more results and code please visit our project website at https://niopeng.github.io/PAPR-in-Motion/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Peng_PAPR_in_Motion_Seamless_Point-level_3D_Scene_Interpolation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_PAPR_in_Motion_Seamless_Point-level_3D_Scene_Interpolation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_PAPR_in_Motion_Seamless_Point-level_3D_Scene_Interpolation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Peng_PAPR_in_Motion_CVPR_2024_supplemental.pdf | null |
Causal Mode Multiplexer: A Novel Framework for Unbiased Multispectral Pedestrian Detection | Taeheon Kim, Sebin Shin, Youngjoon Yu, Hak Gu Kim, Yong Man Ro | RGBT multispectral pedestrian detection has emerged as a promising solution for safety-critical applications that require day/night operations. However the modality bias problem remains unsolved as multispectral pedestrian detectors learn the statistical bias in datasets. Specifically datasets in multispectral pedestrian detection mainly distribute between ROTO (day) and RXTO (night) data; the majority of the pedestrian labels statistically co-occur with their thermal features. As a result multispectral pedestrian detectors show poor generalization ability on examples beyond this statistical correlation such as ROTX data. To address this problem we propose a novel Causal Mode Multiplexer (CMM) framework that effectively learns the causalities between multispectral inputs and predictions. Moreover we construct a new dataset (ROTX-MP) to evaluate modality bias in multispectral pedestrian detection. ROTX-MP mainly includes ROTX examples not presented in previous datasets. Extensive experiments demonstrate that our proposed CMM framework generalizes well on existing datasets (KAIST CVC-14 FLIR) and the new ROTX-MP. Our code and dataset are available open-source. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Causal_Mode_Multiplexer_A_Novel_Framework_for_Unbiased_Multispectral_Pedestrian_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01300 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Causal_Mode_Multiplexer_A_Novel_Framework_for_Unbiased_Multispectral_Pedestrian_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Causal_Mode_Multiplexer_A_Novel_Framework_for_Unbiased_Multispectral_Pedestrian_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Causal_Mode_Multiplexer_CVPR_2024_supplemental.pdf | null |
HIMap: HybrId Representation Learning for End-to-end Vectorized HD Map Construction | Yi Zhou, Hui Zhang, Jiaqian Yu, Yifan Yang, Sangil Jung, Seung-In Park, ByungIn Yoo | Vectorized High-Definition (HD) map construction requires predictions of the category and point coordinates of map elements (e.g. road boundary lane divider pedestrian crossing etc.). State-of-the-art methods are mainly based on point-level representation learning for regressing accurate point coordinates. However this pipeline has limitations in obtaining element-level information and handling element-level failures e.g. erroneous element shape or entanglement between elements. To tackle the above issues we propose a simple yet effective HybrId framework named HIMap to sufficiently learn and interact both point-level and element-level information. Concretely we introduce a hybrid representation called HIQuery to represent all map elements and propose a point-element interactor to interactively extract and encode the hybrid information of elements e.g. point position and element shape into the HIQuery. Additionally we present a point-element consistency constraint to enhance the consistency between the point-level and element-level information. Finally the output point-element integrated HIQuery can be directly converted into map elements' class point coordinates and mask. We conduct extensive experiments and consistently outperform previous methods on both nuScenes and Argoverse2 datasets. Notably our method achieves 77.8 mAP on the nuScenes dataset remarkably superior to previous SOTAs by 8.3 mAP at least. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_HIMap_HybrId_Representation_Learning_for_End-to-end_Vectorized_HD_Map_Construction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.08639 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_HIMap_HybrId_Representation_Learning_for_End-to-end_Vectorized_HD_Map_Construction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_HIMap_HybrId_Representation_Learning_for_End-to-end_Vectorized_HD_Map_Construction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_HIMap_HybrId_Representation_CVPR_2024_supplemental.pdf | null |
LTA-PCS: Learnable Task-Agnostic Point Cloud Sampling | Jiaheng Liu, Jianhao Li, Kaisiyuan Wang, Hongcheng Guo, Jian Yang, Junran Peng, Ke Xu, Xianglong Liu, Jinyang Guo | Recently many approaches directly operate on point clouds for different tasks. These approaches become more computation and storage demanding when point cloud size is large. To reduce the required computation and storage one possible solution is to sample the point cloud. In this paper we propose the first Learnable Task-Agnostic Point Cloud Sampling (LTA-PCS) framework. Existing task-agnostic point cloud sampling strategy (e.g. FPS) does not consider semantic information of point clouds causing degraded performance on downstream tasks. While learning-based point cloud sampling methods consider semantic information they are task-specific and require task-oriented ground-truth annotations. So they cannot generalize well on different downstream tasks. Our LTA-PCS achieves task-agnostic point cloud sampling without requiring task-oriented labels in which both the geometric and semantic information of points is considered in sampling. Extensive experiments on multiple downstream tasks demonstrate the effectiveness of our LTA-PCS. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_LTA-PCS_Learnable_Task-Agnostic_Point_Cloud_Sampling_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_LTA-PCS_Learnable_Task-Agnostic_Point_Cloud_Sampling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_LTA-PCS_Learnable_Task-Agnostic_Point_Cloud_Sampling_CVPR_2024_paper.html | CVPR 2024 | null | null |
Non-Rigid Structure-from-Motion: Temporally-Smooth Procrustean Alignment and Spatially-Variant Deformation Modeling | Jiawei Shi, Hui Deng, Yuchao Dai | Even though Non-rigid Structure-from-Motion (NRSfM) has been extensively studied and great progress has been made there are still key challenges that hinder their broad real-world applications: 1) the inherent motion/rotation ambiguity requires either explicit camera motion recovery with extra constraint or complex Procrustean Alignment; 2) existing low-rank modeling of the global shape can over-penalize drastic deformations in the 3D shape sequence. This paper proposes to resolve the above issues from a spatial-temporal modeling perspective. First we propose a novel Temporally-smooth Procrustean Alignment module that estimates 3D deforming shapes and adjusts the camera motion by aligning the 3D shape sequence consecutively. Our new alignment module remedies the requirement of complex reference 3D shape during alignment which is more conductive to non-isotropic deformation modeling. Second we propose a spatial-weighted approach to enforce the low-rank constraint adaptively at different locations to accommodate drastic spatially-variant deformation reconstruction better. Our modeling outperform existing low-rank based methods and extensive experiments across different datasets validate the effectiveness of our method. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shi_Non-Rigid_Structure-from-Motion_Temporally-Smooth_Procrustean_Alignment_and_Spatially-Variant_Deformation_Modeling_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shi_Non-Rigid_Structure-from-Motion_Temporally-Smooth_Procrustean_Alignment_and_Spatially-Variant_Deformation_Modeling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shi_Non-Rigid_Structure-from-Motion_Temporally-Smooth_Procrustean_Alignment_and_Spatially-Variant_Deformation_Modeling_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shi_Non-Rigid_Structure-from-Motion_Temporally-Smooth_CVPR_2024_supplemental.pdf | null |
ShapeMatcher: Self-Supervised Joint Shape Canonicalization Segmentation Retrieval and Deformation | Yan Di, Chenyangguang Zhang, Chaowei Wang, Ruida Zhang, Guangyao Zhai, Yanyan Li, Bowen Fu, Xiangyang Ji, Shan Gao | In this paper we present ShapeMatcher a unified self-supervised learning framework for joint shape canonicalization segmentation retrieval and deformation. Given a partially-observed object in an arbitrary pose we first canonicalize the object by extracting point-wise affine invariant features disentangling inherent structure of the object with its pose and size. These learned features are then leveraged to predict semantically consistent part segmentation and corresponding part centers. Next our lightweight retrieval module aggregates the features within each part as its retrieval token and compare all the tokens with source shapes from a pre-established database to identify the most geometrically similar shape. Finally we deform the retrieved shape in the deformation module to tightly fit the input object by harnessing part center guided neural cage deformation. The key insight of ShapeMaker is the simultaneous training of the four highly-associated processes: canonicalization segmentation retrieval and deformation leveraging cross-task consistency losses for mutual supervision. Extensive experiments on synthetic datasets PartNet ComplementMe and real-world dataset Scan2CAD demonstrate that ShapeMatcher surpasses competitors by a large margin. Code is released at https://github.com/Det1999/ShapeMaker. | https://openaccess.thecvf.com/content/CVPR2024/papers/Di_ShapeMatcher_Self-Supervised_Joint_Shape_Canonicalization_Segmentation_Retrieval_and_Deformation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.11106 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Di_ShapeMatcher_Self-Supervised_Joint_Shape_Canonicalization_Segmentation_Retrieval_and_Deformation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Di_ShapeMatcher_Self-Supervised_Joint_Shape_Canonicalization_Segmentation_Retrieval_and_Deformation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Di_ShapeMatcher_Self-Supervised_Joint_CVPR_2024_supplemental.pdf | null |
UniPTS: A Unified Framework for Proficient Post-Training Sparsity | Jingjing Xie, Yuxin Zhang, Mingbao Lin, Liujuan Cao, Rongrong Ji | Post-training Sparsity (PTS) is a recently emerged avenue that chases efficient network sparsity with limited data in need. Existing PTS methods however undergo significant performance degradation compared with traditional methods that retrain the sparse networks via the whole dataset especially at high sparsity ratios. In this paper we attempt to reconcile this disparity by transposing three cardinal factors that profoundly alter the performance of conventional sparsity into the context of PTS. Our endeavors particularly comprise (1) A base-decayed sparsity objective that promotes efficient knowledge transferring from dense network to the sparse counterpart. (2) A reducing-regrowing search algorithm designed to ascertain the optimal sparsity distribution while circumventing overfitting to the small calibration set in PTS. (3) The employment of dynamic sparse training predicated on the preceding aspects aimed at comprehensively optimizing the sparsity structure while ensuring training stability. Our proposed framework termed UniPTS is validated to be much superior to existing PTS methods across extensive benchmarks. As an illustration it amplifies the performance of POT a recently proposed recipe from 3.9% to 68.6% when pruning ResNet-50 at 90% sparsity ratio on ImageNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_UniPTS_A_Unified_Framework_for_Proficient_Post-Training_Sparsity_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.18810 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_UniPTS_A_Unified_Framework_for_Proficient_Post-Training_Sparsity_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_UniPTS_A_Unified_Framework_for_Proficient_Post-Training_Sparsity_CVPR_2024_paper.html | CVPR 2024 | null | null |
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