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SpikingResformer: Bridging ResNet and Vision Transformer in Spiking Neural Networks | Xinyu Shi, Zecheng Hao, Zhaofei Yu | The remarkable success of Vision Transformers in Artificial Neural Networks (ANNs) has led to a growing interest in incorporating the self-attention mechanism and transformer-based architecture into Spiking Neural Networks (SNNs). While existing methods propose spiking self-attention mechanisms that are compatible with SNNs they lack reasonable scaling methods and the overall architectures proposed by these methods suffer from a bottleneck in effectively extracting local features. To address these challenges we propose a novel spiking self-attention mechanism named Dual Spike Self-Attention (DSSA) with a reasonable scaling method. Based on DSSA we propose a novel spiking Vision Transformer architecture called SpikingResformer which combines the ResNet-based multi-stage architecture with our proposed DSSA to improve both performance and energy efficiency while reducing parameters. Experimental results show that SpikingResformer achieves higher accuracy with fewer parameters and lower energy consumption than other spiking Vision Transformer counterparts. Notably our SpikingResformer-L achieves 79.40% top-1 accuracy on ImageNet with 4 time-steps which is the state-of-the-art result in the SNN field. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shi_SpikingResformer_Bridging_ResNet_and_Vision_Transformer_in_Spiking_Neural_Networks_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.14302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shi_SpikingResformer_Bridging_ResNet_and_Vision_Transformer_in_Spiking_Neural_Networks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shi_SpikingResformer_Bridging_ResNet_and_Vision_Transformer_in_Spiking_Neural_Networks_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shi_SpikingResformer_Bridging_ResNet_CVPR_2024_supplemental.pdf | null |
Scene Adaptive Sparse Transformer for Event-based Object Detection | Yansong Peng, Hebei Li, Yueyi Zhang, Xiaoyan Sun, Feng Wu | While recent Transformer-based approaches have shown impressive performances on event-based object detection tasks their high computational costs still diminish the low power consumption advantage of event cameras. Image-based works attempt to reduce these costs by introducing sparse Transformers. However they display inadequate sparsity and adaptability when applied to event-based object detection since these approaches cannot balance the fine granularity of token-level sparsification and the efficiency of window-based Transformers leading to reduced performance and efficiency. Furthermore they lack scene-specific sparsity optimization resulting in information loss and a lower recall rate. To overcome these limitations we propose the Scene Adaptive Sparse Transformer (SAST). SAST enables window-token co-sparsification significantly enhancing fault tolerance and reducing computational overhead. Leveraging the innovative scoring and selection modules along with the Masked Sparse Window Self-Attention SAST showcases remarkable scene-aware adaptability: It focuses only on important objects and dynamically optimizes sparsity level according to scene complexity maintaining a remarkable balance between performance and computational cost. The evaluation results show that SAST outperforms all other dense and sparse networks in both performance and efficiency on two large-scale event-based object detection datasets (1Mpx and Gen1). Code: https://github.com/Peterande/SAST | https://openaccess.thecvf.com/content/CVPR2024/papers/Peng_Scene_Adaptive_Sparse_Transformer_for_Event-based_Object_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01882 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_Scene_Adaptive_Sparse_Transformer_for_Event-based_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_Scene_Adaptive_Sparse_Transformer_for_Event-based_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Peng_Scene_Adaptive_Sparse_CVPR_2024_supplemental.zip | null |
Gaussian Shadow Casting for Neural Characters | Luis Bolanos, Shih-Yang Su, Helge Rhodin | Neural character models can now reconstruct detailed geometry and texture from video but they lack explicit shadows and shading leading to artifacts when generating novel views and poses or during relighting. It is particularly difficult to include shadows as they are a global effect and the required casting of secondary rays is costly. We propose a new shadow model using a Gaussian density proxy that replaces sampling with a simple analytic formula. It supports dynamic motion and is tailored for shadow computation thereby avoiding the affine projection approximation and sorting required by the closely related Gaussian splatting. Combined with a deferred neural rendering model our Gaussian shadows enable Lambertian shading and shadow casting with minimal overhead. We demonstrate improved reconstructions with better separation of albedo shading and shadows in challenging outdoor scenes with direct sun light and hard shadows. Our method is able to optimize the light direction without any input from the user. As a result novel poses have fewer shadow artifacts and relighting in novel scenes is more realistic compared to the state-of-the-art methods providing new ways to pose neural characters in novel environments increasing their applicability. Code available at: https://github.com/LuisBolanos17/GaussianShadowCasting | https://openaccess.thecvf.com/content/CVPR2024/papers/Bolanos_Gaussian_Shadow_Casting_for_Neural_Characters_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.06116 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bolanos_Gaussian_Shadow_Casting_for_Neural_Characters_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bolanos_Gaussian_Shadow_Casting_for_Neural_Characters_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bolanos_Gaussian_Shadow_Casting_CVPR_2024_supplemental.zip | null |
CURSOR: Scalable Mixed-Order Hypergraph Matching with CUR Decomposition | Qixuan Zheng, Ming Zhang, Hong Yan | To achieve greater accuracy hypergraph matching algorithms require exponential increases in computational resources. Recent kd-tree-based approximate nearest neighbor (ANN) methods despite the sparsity of their compatibility tensor still require exhaustive calculations for large-scale graph matching. This work utilizes CUR tensor decomposition and introduces a novel cascaded second and third-order hypergraph matching framework (CURSOR) for efficient hypergraph matching. A CUR-based second-order graph matching algorithm is used to provide a rough match and then the core of CURSOR a fiber-CUR-based tensor generation method directly calculates entries of the compatibility tensor by leveraging the initial second-order match result. This significantly decreases the time complexity and tensor density. A probability relaxation labeling (PRL)-based matching algorithm especially suitable for sparse tensors is developed. Experiment results on large-scale synthetic datasets and widely-adopted benchmark sets demonstrate the superiority of CURSOR over existing methods. The tensor generation method in CURSOR can be integrated seamlessly into existing hypergraph matching methods to improve their performance and lower their computational costs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zheng_CURSOR_Scalable_Mixed-Order_Hypergraph_Matching_with_CUR_Decomposition_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.16594 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_CURSOR_Scalable_Mixed-Order_Hypergraph_Matching_with_CUR_Decomposition_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_CURSOR_Scalable_Mixed-Order_Hypergraph_Matching_with_CUR_Decomposition_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zheng_CURSOR_Scalable_Mixed-Order_CVPR_2024_supplemental.pdf | null |
Federated Online Adaptation for Deep Stereo | Matteo Poggi, Fabio Tosi | We introduce a novel approach for adapting deep stereo networks in a collaborative manner. By building over principles of federated learning we develop a distributed framework allowing for demanding the optimization process to a number of clients deployed in different environments. This makes it possible for a deep stereo network running on resourced-constrained devices to capitalize on the adaptation process carried out by other instances of the same architecture and thus improve its accuracy in challenging environments even when it cannot carry out adaptation on its own. Experimental results show how federated adaptation performs equivalently to on-device adaptation and even better when dealing with challenging environments. | https://openaccess.thecvf.com/content/CVPR2024/papers/Poggi_Federated_Online_Adaptation_for_Deep_Stereo_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.14873 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Poggi_Federated_Online_Adaptation_for_Deep_Stereo_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Poggi_Federated_Online_Adaptation_for_Deep_Stereo_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Poggi_Federated_Online_Adaptation_CVPR_2024_supplemental.pdf | null |
Sequential Modeling Enables Scalable Learning for Large Vision Models | Yutong Bai, Xinyang Geng, Karttikeya Mangalam, Amir Bar, Alan L. Yuille, Trevor Darrell, Jitendra Malik, Alexei A. Efros | We introduce a novel sequential modeling approach which enables learning a Large Vision Model (LVM) without making use of any linguistic data. To do this we define a common format "visual sentences" in which we can represent raw images and videos as well as annotated data sources such as semantic segmentations and depth reconstructions without needing any meta-knowledge beyond the pixels. Once this wide variety of visual data (comprising 420 billion tokens) is represented as sequences the model can be trained to minimize a cross-entropy loss for next token prediction. By training across various scales of model architecture and data diversity we provide empirical evidence that our models scale effectively. Many different vision tasks can be solved by designing suitable visual prompts at test time. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bai_Sequential_Modeling_Enables_Scalable_Learning_for_Large_Vision_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.00785 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bai_Sequential_Modeling_Enables_Scalable_Learning_for_Large_Vision_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bai_Sequential_Modeling_Enables_Scalable_Learning_for_Large_Vision_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bai_Sequential_Modeling_Enables_CVPR_2024_supplemental.pdf | null |
Self-Supervised Dual Contouring | Ramana Sundararaman, Roman Klokov, Maks Ovsjanikov | Learning-based isosurface extraction methods have recently emerged as a robust and efficient alternative to axiomatic techniques. However the vast majority of such approaches rely on supervised training with axiomatically computed ground truths thus potentially inheriting biases and data artefacts of the corresponding axiomatic methods. Steering away from such dependencies we propose a self-supervised training scheme to the Neural Dual Contouring meshing framework resulting in our method: Self-Supervised Dual Contouring (SDC). Instead of optimizing predicted mesh vertices with supervised training we use two novel self-supervised loss functions that encourage the consistency between distances to the generated mesh up to the first order. Meshes reconstructed by SDC surpass existing data-driven methods in capturing intricate details while being more robust to possible irregularities in the input. Furthermore we use the same self-supervised training objective linking inferred mesh and input SDF to regularize the training process of Deep Implicit Networks (DINs). We demonstrate that the resulting DINs produce higher-quality implicit functions ultimately leading to more accurate and detail-preserving surfaces compared to prior baselines for different input modalities. Finally we demonstrate that our self-supervised losses improve meshing performance in the single-view reconstruction task by enabling joint training of predicted SDF and resulting output mesh. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sundararaman_Self-Supervised_Dual_Contouring_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.18131 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sundararaman_Self-Supervised_Dual_Contouring_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sundararaman_Self-Supervised_Dual_Contouring_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sundararaman_Self-Supervised_Dual_Contouring_CVPR_2024_supplemental.pdf | null |
Regularized Parameter Uncertainty for Improving Generalization in Reinforcement Learning | Pehuen Moure, Longbiao Cheng, Joachim Ott, Zuowen Wang, Shih-Chii Liu | In order for reinforcement learning (RL) agents to be deployed in real-world environments they must be able to generalize to unseen environments. However RL struggles with out-of-distribution generalization often due to over-fitting the particulars of the training environment. Although regularization techniques from supervised learning can be applied to avoid over-fitting the differences between supervised learning and RL limit their application. To address this we propose the Signal-to-Noise Ratio regulated Parameter Uncertainty Network (SNR PUN) for RL. We introduce SNR as a new measure of regularizing the parameter uncertainty of a network and provide a formal analysis explaining why SNR regularization works well for RL. We demonstrate the effectiveness of our proposed method to generalize in several simulated environments; and in a physical system showing the possibility of using SNR PUN for applying RL to real-world applications. | https://openaccess.thecvf.com/content/CVPR2024/papers/Moure_Regularized_Parameter_Uncertainty_for_Improving_Generalization_in_Reinforcement_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Moure_Regularized_Parameter_Uncertainty_for_Improving_Generalization_in_Reinforcement_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Moure_Regularized_Parameter_Uncertainty_for_Improving_Generalization_in_Reinforcement_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Moure_Regularized_Parameter_Uncertainty_CVPR_2024_supplemental.pdf | null |
GigaTraj: Predicting Long-term Trajectories of Hundreds of Pedestrians in Gigapixel Complex Scenes | Haozhe Lin, Chunyu Wei, Li He, Yuchen Guo, Yunqi Zhao, Shanglong Li, Lu Fang | Pedestrian trajectory prediction is a well-established task with significant recent advancements. However existing datasets are unable to fulfill the demand for studying minute-level long-term trajectory prediction mainly due to the lack of high-resolution trajectory observation in the wide field of view (FoV). To bridge this gap we introduce a novel dataset named GigaTraj featuring videos capturing a wide FoV with ~ 4 x10^4 m^2 and high-resolution imagery at the gigapixel level. Furthermore GigaTraj includes comprehensive annotations such as bounding boxes identity associations world coordinates group/interaction relationships and scene semantics. Leveraging these multimodal annotations we evaluate and validate the state-of-the-art approaches for minute-level long-term trajectory prediction in large-scale scenes. Extensive experiments and analyses have revealed that long-term prediction for pedestrian trajectories presents numerous challenges indicating a vital new direction for trajectory research. The dataset is available at www.gigavision.ai. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lin_GigaTraj_Predicting_Long-term_Trajectories_of_Hundreds_of_Pedestrians_in_Gigapixel_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lin_GigaTraj_Predicting_Long-term_Trajectories_of_Hundreds_of_Pedestrians_in_Gigapixel_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lin_GigaTraj_Predicting_Long-term_Trajectories_of_Hundreds_of_Pedestrians_in_Gigapixel_CVPR_2024_paper.html | CVPR 2024 | null | null |
GSVA: Generalized Segmentation via Multimodal Large Language Models | Zhuofan Xia, Dongchen Han, Yizeng Han, Xuran Pan, Shiji Song, Gao Huang | Generalized Referring Expression Segmentation (GRES) extends the scope of classic RES to refer to multiple objects in one expression or identify the empty targets absent in the image. GRES poses challenges in modeling the complex spatial relationships of the instances in the image and identifying non-existing referents. Multimodal Large Language Models (MLLMs) have recently shown tremendous progress in these complicated vision-language tasks. Connecting Large Language Models (LLMs) and vision models MLLMs are proficient in understanding contexts with visual inputs. Among them LISA as a representative adopts a special [SEG] token to prompt a segmentation mask decoder e.g. SAM to enable MLLMs in the RES task. However existing solutions to GRES remain unsatisfactory since current segmentation MLLMs cannot correctly handle the cases where users might reference multiple subjects in a singular prompt or provide descriptions incongruent with any image target. In this paper we propose Generalized Segmentation Vision Assistant (GSVA) to address this gap. Specifically GSVA reuses the [SEG] token to prompt the segmentation model towards supporting multiple mask references simultaneously and innovatively learns to generate a [REJ] token to reject the targets explicitly. Experiments validate GSVA's efficacy in resolving the GRES issue marking a notable enhancement and setting a new record on the GRES benchmark gRefCOCO dataset. GSVA also proves effective across various classic referring segmentation and comprehension tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xia_GSVA_Generalized_Segmentation_via_Multimodal_Large_Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.10103 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_GSVA_Generalized_Segmentation_via_Multimodal_Large_Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_GSVA_Generalized_Segmentation_via_Multimodal_Large_Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xia_GSVA_Generalized_Segmentation_CVPR_2024_supplemental.pdf | null |
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution | Cheeun Hong, Kyoung Mu Lee | Although image super-resolution (SR) problem has experienced unprecedented restoration accuracy with deep neural networks it has yet limited versatile applications due to the substantial computational costs. Since different input images for SR face different restoration difficulties adapting computational costs based on the input image referred to as adaptive inference has emerged as a promising solution to compress SR networks. Specifically adapting the quantization bit-widths has successfully reduced the inference and memory cost without sacrificing the accuracy. However despite the benefits of the resultant adaptive network existing works rely on time-intensive quantization-aware training with full access to the original training pairs to learn the appropriate bit allocation policies which limits its ubiquitous usage. To this end we introduce the first on-the-fly adaptive quantization framework that accelerates the processing time from hours to seconds. We formulate the bit allocation problem with only two bit mapping modules: one to map the input image to the image-wise bit adaptation factor and one to obtain the layer-wise adaptation factors. These bit mappings are calibrated and fine-tuned using only a small number of calibration images. We achieve competitive performance with the previous adaptive quantization methods while the processing time is accelerated by x2000. Codes are available at https://github.com/Cheeun/AdaBM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_AdaBM_On-the-Fly_Adaptive_Bit_Mapping_for_Image_Super-Resolution_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.03296 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hong_AdaBM_On-the-Fly_Adaptive_Bit_Mapping_for_Image_Super-Resolution_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hong_AdaBM_On-the-Fly_Adaptive_Bit_Mapping_for_Image_Super-Resolution_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hong_AdaBM_On-the-Fly_Adaptive_CVPR_2024_supplemental.pdf | null |
CoralSCOP: Segment any COral Image on this Planet | Ziqiang Zheng, Haixin Liang, Binh-Son Hua, Yue Him Wong, Put Ang Jr, Apple Pui Yi Chui, Sai-Kit Yeung | Underwater visual understanding has recently gained increasing attention within the computer vision community for studying and monitoring underwater ecosystems. Among these coral reefs play an important and intricate role often referred to as the rainforests of the sea due to their rich biodiversity and crucial environmental impact. Existing coral analysis due to its technical complexity requires significant manual work from coral biologists therefore hindering scalable and comprehensive studies. In this paper we introduce CoralSCOP the first foundation model designed for the automatic dense segmentation of coral reefs. CoralSCOP is developed to accurately assign labels to different coral entities addressing the challenges in the semantic analysis of coral imagery. Its main objective is to identify and delineate the irregular boundaries between various coral individuals across different granularities such as coral/non-coral growth form and genus. This task is challenging due to the semantic agnostic nature or fixed limited semantic categories of previous generic segmentation methods which fail to adequately capture the complex characteristics of coral structures. By introducing a novel parallel semantic branch CoralSCOP can produce high-quality coral masks with semantics that enable a wide range of downstream coral reef analysis tasks. We demonstrate that CoralSCOP exhibits a strong zero-shot ability to segment unseen coral images. To effectively train our foundation model we propose CoralMask a new dataset with 41297 densely labeled coral images and 330144 coral masks. We have conducted comprehensive and extensive experiments to demonstrate the advantages of CoralSCOP over existing generalist segmentation algorithms and coral reef analytical approaches. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zheng_CoralSCOP_Segment_any_COral_Image_on_this_Planet_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_CoralSCOP_Segment_any_COral_Image_on_this_Planet_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_CoralSCOP_Segment_any_COral_Image_on_this_Planet_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zheng_CoralSCOP_Segment_any_CVPR_2024_supplemental.pdf | null |
SVGDreamer: Text Guided SVG Generation with Diffusion Model | Ximing Xing, Haitao Zhou, Chuang Wang, Jing Zhang, Dong Xu, Qian Yu | Recently text-guided scalable vector graphics (SVGs) synthesis has shown promise in domains such as iconography and sketch. However existing text-to-SVG generation methods lack editability and struggle with visual quality and result diversity. To address these limitations we propose a novel text-guided vector graphics synthesis method called SVGDreamer. SVGDreamer incorporates a semantic-driven image vectorization (SIVE) process that enables the decomposition of synthesis into foreground objects and background thereby enhancing editability. Specifically the SIVE process introduces attention-based primitive control and an attention-mask loss function for effective control and manipulation of individual elements. Additionally we propose a Vectorized Particle-based Score Distillation (VPSD) approach to address issues of shape over-smoothing color over-saturation limited diversity and slow convergence of the existing text-to-SVG generation methods by modeling SVGs as distributions of control points and colors. Furthermore VPSD leverages a reward model to re-weight vector particles which improves aesthetic appeal and accelerates convergence. Extensive experiments are conducted to validate the effectiveness of SVGDreamer demonstrating its superiority over baseline methods in terms of editability visual quality and diversity. Project page: \href https://ximinng.github.io/SVGDreamer-project/ https://ximinng.github.io/SVGDreamer-project/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Xing_SVGDreamer_Text_Guided_SVG_Generation_with_Diffusion_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.16476 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xing_SVGDreamer_Text_Guided_SVG_Generation_with_Diffusion_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xing_SVGDreamer_Text_Guided_SVG_Generation_with_Diffusion_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xing_SVGDreamer_Text_Guided_CVPR_2024_supplemental.pdf | null |
BlockGCN: Redefine Topology Awareness for Skeleton-Based Action Recognition | Yuxuan Zhou, Xudong Yan, Zhi-Qi Cheng, Yan Yan, Qi Dai, Xian-Sheng Hua | Graph Convolutional Networks (GCNs) have long set the state-of-the-art in skeleton-based action recognition leveraging their ability to unravel the complex dynamics of human joint topology through the graph's adjacency matrix. However an inherent flaw has come to light in these cutting-edge models: they tend to optimize the adjacency matrix jointly with the model weights. This process while seemingly efficient causes a gradual decay of bone connectivity data resulting in a model indifferent to the very topology it sought to represent. To remedy this we propose a two-fold strategy: (1) We introduce an innovative approach that encodes bone connectivity by harnessing the power of graph distances to describe the physical topology; we further incorporate action-specific topological representation via persistent homology analysis to depict systemic dynamics. This preserves the vital topological nuances often lost in conventional GCNs. (2) Our investigation also reveals the redundancy in existing GCNs for multi-relational modeling which we address by proposing an efficient refinement to Graph Convolutions (GC) - the BlockGC. This significantly reduces parameters while improving performance beyond original GCNs. Our full model BlockGCN establishes new benchmarks in skeleton-based action recognition across all model categories. Its high accuracy and lightweight design most notably on the large-scale NTU RGB+D 120 dataset stand as strong validation of the efficacy of BlockGCN. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_BlockGCN_Redefine_Topology_Awareness_for_Skeleton-Based_Action_Recognition_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_BlockGCN_Redefine_Topology_Awareness_for_Skeleton-Based_Action_Recognition_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_BlockGCN_Redefine_Topology_Awareness_for_Skeleton-Based_Action_Recognition_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_BlockGCN_Redefine_Topology_CVPR_2024_supplemental.pdf | null |
Improved Baselines with Visual Instruction Tuning | Haotian Liu, Chunyuan Li, Yuheng Li, Yong Jae Lee | Large multimodal models (LMM) have recently shown encouraging progress with visual instruction tuning. In this paper we present the first systematic study to investigate the design choices of LMMs in a controlled setting under the LLaVA framework. We show that the fully-connected vision-language connector in LLaVA is surprisingly powerful and data-efficient. With simple modifications to LLaVA namely using CLIP-ViT-L-336px with an MLP projection and adding academic-task-oriented VQA data with response formatting prompts we establish stronger baselines that achieve state-of-the-art across 11 benchmarks. Our final 13B checkpoint uses merely 1.2M publicly available data and finishes full training in 1 day on a single 8-A100 node. Furthermore we present some early exploration of open problems in LMMs including scaling to higher resolution inputs compositional capabilities and model hallucination etc. We hope this makes state-of-the-art LMM research more accessible. Code and model will be publicly available. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Improved_Baselines_with_Visual_Instruction_Tuning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.03744 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Improved_Baselines_with_Visual_Instruction_Tuning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Improved_Baselines_with_Visual_Instruction_Tuning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Improved_Baselines_with_CVPR_2024_supplemental.pdf | null |
Structure-Guided Adversarial Training of Diffusion Models | Ling Yang, Haotian Qian, Zhilong Zhang, Jingwei Liu, Bin Cui | Diffusion models have demonstrated exceptional efficacy in various generative applications. While existing models focus on minimizing a weighted sum of denoising score matching losses for data distribution modeling their training primarily emphasizes instance-level optimization overlooking valuable structural information within each mini-batch indicative of pair-wise relationships among samples. To address this limitation we introduce Structure-guided Adversarial training of Diffusion Models (SADM). In this pioneering approach we compel the model to learn manifold structures between samples in each training batch. To ensure the model captures authentic manifold structures in the data distribution we advocate adversarial training of the diffusion generator against a novel structure discriminator in a minimax game distinguishing real manifold structures from the generated ones. SADM substantially outperforms existing methods in image generation and cross-domain fine-tuning tasks across 12 datasets establishing a new state-of-the-art FID of 1.58 and 2.11 on ImageNet for class-conditional image generation at resolutions of 256x256 and 512x512 respectively. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Structure-Guided_Adversarial_Training_of_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17563 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Structure-Guided_Adversarial_Training_of_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Structure-Guided_Adversarial_Training_of_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_Structure-Guided_Adversarial_Training_CVPR_2024_supplemental.pdf | null |
NIFTY: Neural Object Interaction Fields for Guided Human Motion Synthesis | Nilesh Kulkarni, Davis Rempe, Kyle Genova, Abhijit Kundu, Justin Johnson, David Fouhey, Leonidas Guibas | We address the problem of generating realistic 3D motions of humans interacting with objects in a scene. Our key idea is to create a neural interaction field attached to a specific object which outputs the distance to the valid interaction manifold given a human pose as input. This interaction field guides the sampling of an object-conditioned human motion diffusion model so as to encourage plausible contacts and affordance semantics. To support interactions with scarcely available data we propose an automated synthetic data pipeline. For this we seed a pre-trained motion model which has priors for the basics of human movement with interaction-specific anchor poses extracted from limited motion capture data. Using our guided diffusion model trained on generated synthetic data we synthesize realistic motions for sitting and lifting with several objects outperforming alternative approaches in terms of motion quality and successful action completion. We call our framework NIFTY: Neural Interaction Fields for Trajectory sYnthesis. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kulkarni_NIFTY_Neural_Object_Interaction_Fields_for_Guided_Human_Motion_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2307.07511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kulkarni_NIFTY_Neural_Object_Interaction_Fields_for_Guided_Human_Motion_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kulkarni_NIFTY_Neural_Object_Interaction_Fields_for_Guided_Human_Motion_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kulkarni_NIFTY_Neural_Object_CVPR_2024_supplemental.pdf | null |
C2KD: Bridging the Modality Gap for Cross-Modal Knowledge Distillation | Fushuo Huo, Wenchao Xu, Jingcai Guo, Haozhao Wang, Song Guo | Existing Knowledge Distillation (KD) methods typically focus on transferring knowledge from a large-capacity teacher to a low-capacity student model achieving substantial success in unimodal knowledge transfer. However existing methods can hardly be extended to Cross-Modal Knowledge Distillation (CMKD) where the knowledge is transferred from a teacher modality to a different student modality with inference only on the distilled student modality. We empirically reveal that the modality gap i.e. modality imbalance and soft label misalignment incurs the ineffectiveness of traditional KD in CMKD. As a solution we propose a novel \underline C ustomized \underline C rossmodal \underline K nowledge \underline D istillation (C^2KD). Specifically to alleviate the modality gap the pre-trained teacher performs bidirectional distillation with the student to provide customized knowledge. The On-the-Fly Selection Distillation(OFSD) strategy is applied to selectively filter out the samples with misaligned soft labels where we distill cross-modal knowledge from non-target classes to avoid the modality imbalance issue. To further provide receptive cross-modal knowledge proxy student and teacher inheriting unimodal and cross-modal knowledge is formulated to progressively transfer cross-modal knowledge through bidirectional distillation. Experimental results on audio-visual image-text and RGB-depth datasets demonstrate that our method can effectively transfer knowledge across modalities achieving superior performance against traditional KD by a large margin. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huo_C2KD_Bridging_the_Modality_Gap_for_Cross-Modal_Knowledge_Distillation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huo_C2KD_Bridging_the_Modality_Gap_for_Cross-Modal_Knowledge_Distillation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huo_C2KD_Bridging_the_Modality_Gap_for_Cross-Modal_Knowledge_Distillation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huo_C2KD_Bridging_the_CVPR_2024_supplemental.pdf | null |
Traceable Federated Continual Learning | Qiang Wang, Bingyan Liu, Yawen Li | Federated continual learning (FCL) is a typical mechanism to achieve collaborative model training among clients that own dynamic data. While traditional FCL methods have been proved effective they do not consider the task repeatability and fail to achieve good performance under this practical scenario. In this paper we propose a new paradigm namely Traceable Federated Continual Learning (TFCL) aiming to cope with repetitive tasks by tracing and augmenting them. Following the new paradigm we develop TagFed a framework that enables accurate and effective Tracing augmentation and Federation for TFCL. The key idea is to decompose the whole model into a series of marked sub-models for optimizing each client task before conducting group-wise knowledge aggregation such that the repetitive tasks can be located precisely and federated selectively for improved performance. Extensive experiments on our constructed benchmark demonstrate the effectiveness and efficiency of the proposed framework. We will release our code at: https://github.com/P0werWeirdo/TagFCL. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Traceable_Federated_Continual_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Traceable_Federated_Continual_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Traceable_Federated_Continual_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Traceable_Federated_Continual_CVPR_2024_supplemental.pdf | null |
Can Language Beat Numerical Regression? Language-Based Multimodal Trajectory Prediction | Inhwan Bae, Junoh Lee, Hae-Gon Jeon | Language models have demonstrated impressive ability in context understanding and generative performance. Inspired by the recent success of language foundation models in this paper we propose LMTraj (Language-based Multimodal Trajectory predictor) which recasts the trajectory prediction task into a sort of question-answering problem. Departing from traditional numerical regression models which treat the trajectory coordinate sequence as continuous signals we consider them as discrete signals like text prompts. Specially we first transform an input space for the trajectory coordinate into the natural language space. Here the entire time-series trajectories of pedestrians are converted into a text prompt and scene images are described as text information through image captioning. The transformed numerical and image data are then wrapped into the question-answering template for use in a language model. Next to guide the language model in understanding and reasoning high-level knowledge such as scene context and social relationships between pedestrians we introduce an auxiliary multi-task question and answering. We then train a numerical tokenizer with the prompt data. We encourage the tokenizer to separate the integer and decimal parts well and leverage it to capture correlations between the consecutive numbers in the language model. Lastly we train the language model using the numerical tokenizer and all of the question-answer prompts. Here we propose a beam-search-based most-likely prediction and a temperature-based multimodal prediction to implement both deterministic and stochastic inferences. Applying our LMTraj we show that the language-based model can be a powerful pedestrian trajectory predictor and outperforms existing numerical-based predictor methods. Extensive experiments show that our LMTraj can successfully understand social relationships and accurately extrapolate the multimodal futures on the public pedestrian trajectory prediction benchmark. Code is publicly available at https://github.com/inhwanbae/LMTrajectory. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bae_Can_Language_Beat_Numerical_Regression_Language-Based_Multimodal_Trajectory_Prediction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18447 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bae_Can_Language_Beat_Numerical_Regression_Language-Based_Multimodal_Trajectory_Prediction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bae_Can_Language_Beat_Numerical_Regression_Language-Based_Multimodal_Trajectory_Prediction_CVPR_2024_paper.html | CVPR 2024 | null | null |
Building Optimal Neural Architectures using Interpretable Knowledge | Keith G. Mills, Fred X. Han, Mohammad Salameh, Shengyao Lu, Chunhua Zhou, Jiao He, Fengyu Sun, Di Niu | Neural Architecture Search is a costly practice. The fact that a search space can span a vast number of design choices with each architecture evaluation taking nontrivial overhead makes it hard for an algorithm to sufficiently explore candidate networks. In this paper we propose AutoBuild a scheme which learns to align the latent embeddings of operations and architecture modules with the ground-truth performance of the architectures they appear in. By doing so AutoBuild is capable of assigning interpretable importance scores to architecture modules such as individual operation features and larger macro operation sequences such that high-performance neural networks can be constructed without any need for search. Through experiments performed on state-of-the-art image classification segmentation and Stable Diffusion models we show that by mining a relatively small set of evaluated architectures AutoBuild can learn to build high-quality architectures directly or help to reduce search space to focus on relevant areas finding better architectures that outperform both the original labeled ones and ones found by search baselines. Code available at https://github.com/Ascend-Research/AutoBuild | https://openaccess.thecvf.com/content/CVPR2024/papers/Mills_Building_Optimal_Neural_Architectures_using_Interpretable_Knowledge_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.13293 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mills_Building_Optimal_Neural_Architectures_using_Interpretable_Knowledge_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mills_Building_Optimal_Neural_Architectures_using_Interpretable_Knowledge_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mills_Building_Optimal_Neural_CVPR_2024_supplemental.pdf | null |
V?: Guided Visual Search as a Core Mechanism in Multimodal LLMs | Penghao Wu, Saining Xie | When we look around and perform complex tasks how we see and selectively process what we see is crucial. However the lack of this visual search mechanism in current multimodal LLMs (MLLMs) hinders their ability to focus on important visual details especially when handling high-resolution and visually crowded images. To address this we introduce V* an LLM-guided visual search mechanism that employs the world knowledge in LLMs for efficient visual querying. When combined with an MLLM this mechanism enhances collaborative reasoning contextual understanding and precise visual grounding. This integration results in a new MLLM meta-architecture named Show sEArch and TelL (SEAL). We further create V*Bench a benchmark specifically designed to evaluate MLLMs in their ability to process high-resolution images and focus on visual details. Our study highlights the necessity of incorporating visual search capabilities into multimodal systems. The code is available at https://github.com/penghao-wu/vstar | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_V_Guided_Visual_Search_as_a_Core_Mechanism_in_Multimodal_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.14135 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_V_Guided_Visual_Search_as_a_Core_Mechanism_in_Multimodal_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_V_Guided_Visual_Search_as_a_Core_Mechanism_in_Multimodal_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_V_Guided_Visual_CVPR_2024_supplemental.pdf | null |
Unexplored Faces of Robustness and Out-of-Distribution: Covariate Shifts in Environment and Sensor Domains | Eunsu Baek, Keondo Park, Jiyoon Kim, Hyung-Sin Kim | Computer vision applications predict on digital images acquired by a camera from physical scenes through light. However conventional robustness benchmarks rely on perturbations in digitized images diverging from distribution shifts occurring in the image acquisition process. To bridge this gap we introduce a new distribution shift dataset ImageNet-ES comprising variations in environmental and camera sensor factors by directly capturing 202k images with a real camera in a controllable testbed. With the new dataset we evaluate out-of-distribution (OOD) detection and model robustness. We find that existing OOD detection methods do not cope with the covariate shifts in ImageNet-ES implying that the definition and detection of OOD should be revisited to embrace real-world distribution shifts. We also observe that the model becomes more robust in both ImageNet-C and -ES by learning environment and sensor variations in addition to existing digital augmentations. Lastly our results suggest that effective shift mitigation via camera sensor control can significantly improve performance without increasing model size. With these findings our benchmark may aid future research on robustness OOD and camera sensor control for computer vision. Our code and dataset are available at https://github.com/Edw2n/ImageNet-ES. | https://openaccess.thecvf.com/content/CVPR2024/papers/Baek_Unexplored_Faces_of_Robustness_and_Out-of-Distribution_Covariate_Shifts_in_Environment_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Baek_Unexplored_Faces_of_Robustness_and_Out-of-Distribution_Covariate_Shifts_in_Environment_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Baek_Unexplored_Faces_of_Robustness_and_Out-of-Distribution_Covariate_Shifts_in_Environment_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Baek_Unexplored_Faces_of_CVPR_2024_supplemental.pdf | null |
Uncertainty Visualization via Low-Dimensional Posterior Projections | Omer Yair, Elias Nehme, Tomer Michaeli | In ill-posed inverse problems it is commonly desirable to obtain insight into the full spectrum of plausible solutions rather than extracting only a single reconstruction. Information about the plausible solutions and their likelihoods is encoded in the posterior distribution. However for high-dimensional data this distribution is challenging to visualize. In this work we introduce a new approach for estimating and visualizing posteriors by employing energy-based models (EBMs) over low-dimensional subspaces. Specifically we train a conditional EBM that receives an input measurement and a set of directions that span some low-dimensional subspace of solutions and outputs the probability density function of the posterior within that space. We demonstrate the effectiveness of our method across a diverse range of datasets and image restoration problems showcasing its strength in uncertainty quantification and visualization. As we show our method outperforms a baseline that projects samples from a diffusion-based posterior sampler while being orders of magnitude faster. Furthermore it is more accurate than a baseline that assumes a Gaussian posterior. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yair_Uncertainty_Visualization_via_Low-Dimensional_Posterior_Projections_CVPR_2024_paper.pdf | https://arxiv.org/abs/2312.07804 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yair_Uncertainty_Visualization_via_Low-Dimensional_Posterior_Projections_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yair_Uncertainty_Visualization_via_Low-Dimensional_Posterior_Projections_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yair_Uncertainty_Visualization_via_CVPR_2024_supplemental.pdf | https://openaccess.thecvf.com |
VSCode: General Visual Salient and Camouflaged Object Detection with 2D Prompt Learning | Ziyang Luo, Nian Liu, Wangbo Zhao, Xuguang Yang, Dingwen Zhang, Deng-Ping Fan, Fahad Khan, Junwei Han | Salient object detection (SOD) and camouflaged object detection (COD) are related yet distinct binary mapping tasks. These tasks involve multiple modalities sharing commonalities and unique cues. Existing research often employs intricate task-specific specialist models potentially leading to redundancy and suboptimal results. We introduce VSCode a generalist model with novel 2D prompt learning to jointly address four SOD tasks and three COD tasks. We utilize VST as the foundation model and introduce 2D prompts within the encoder-decoder architecture to learn domain and task-specific knowledge on two separate dimensions. A prompt discrimination loss helps disentangle peculiarities to benefit model optimization. VSCode outperforms state-of-the-art methods across six tasks on 26 datasets and exhibits zero-shot generalization to unseen tasks by combining 2D prompts such as RGB-D COD. Source code has been available at https://github.com/Sssssuperior/VSCode. | https://openaccess.thecvf.com/content/CVPR2024/papers/Luo_VSCode_General_Visual_Salient_and_Camouflaged_Object_Detection_with_2D_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.15011 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_VSCode_General_Visual_Salient_and_Camouflaged_Object_Detection_with_2D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_VSCode_General_Visual_Salient_and_Camouflaged_Object_Detection_with_2D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Luo_VSCode_General_Visual_CVPR_2024_supplemental.pdf | null |
GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting | Yiwen Chen, Zilong Chen, Chi Zhang, Feng Wang, Xiaofeng Yang, Yikai Wang, Zhongang Cai, Lei Yang, Huaping Liu, Guosheng Lin | 3D editing plays a crucial role in many areas such as gaming and virtual reality. Traditional 3D editing methods which rely on representations like meshes and point clouds often fall short in realistically depicting complex scenes. On the other hand methods based on implicit 3D representations like Neural Radiance Field (NeRF) render complex scenes effectively but suffer from slow processing speeds and limited control over specific scene areas. In response to these challenges our paper presents GaussianEditor the first 3D editing algorithm based on Gaussian Splatting (GS) a novel 3D representation. GaussianEditor enhances precision and control in editing through our proposed Gaussian semantic tracing which traces the editing target throughout the training process. Additionally we propose Hierarchical Gaussian splatting (HGS) to achieve stabilized and fine results under stochastic generative guidance from 2D diffusion models. We also develop editing strategies for efficient object removal and integration a challenging task for existing methods. Our comprehensive experiments demonstrate GaussianEditor's superior control effective and efficient performance marking a significant advancement in 3D editing. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_GaussianEditor_Swift_and_Controllable_3D_Editing_with_Gaussian_Splatting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.14521 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_GaussianEditor_Swift_and_Controllable_3D_Editing_with_Gaussian_Splatting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_GaussianEditor_Swift_and_Controllable_3D_Editing_with_Gaussian_Splatting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_GaussianEditor_Swift_and_CVPR_2024_supplemental.zip | null |
Holo-Relighting: Controllable Volumetric Portrait Relighting from a Single Image | Yiqun Mei, Yu Zeng, He Zhang, Zhixin Shu, Xuaner Zhang, Sai Bi, Jianming Zhang, HyunJoon Jung, Vishal M. Patel | At the core of portrait photography is the search for ideal lighting and viewpoint. The process often requires advanced knowledge in photography and an elaborate studio setup. In this work we propose Holo-Relighting a volumetric relighting method that is capable of synthesizing novel viewpoints and novel lighting from a single image. Holo-Relighting leverages the pretrained 3D GAN (EG3D) to reconstruct geometry and appearance from an input portrait as a set of 3D-aware features. We design a relighting module conditioned on a given lighting to process these features and predict a relit 3D representation in the form of a tri-plane which can render to an arbitrary viewpoint through volume rendering. Besides viewpoint and lighting control Holo-Relighting also takes the head pose as a condition to enable head-pose-dependent lighting effects. With these novel designs Holo-Relighting can generate complex non-Lambertian lighting effects (e.g. specular highlights and cast shadows) without using any explicit physical lighting priors. We train Holo-Relighting with data captured with a light stage and propose two data-rendering techniques to improve the data quality for training the volumetric relighting system. Through quantitative and qualitative experiments we demonstrate Holo-Relighting can achieve state-of-the-arts relighting quality with better photorealism 3D consistency and controllability. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mei_Holo-Relighting_Controllable_Volumetric_Portrait_Relighting_from_a_Single_Image_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mei_Holo-Relighting_Controllable_Volumetric_Portrait_Relighting_from_a_Single_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mei_Holo-Relighting_Controllable_Volumetric_Portrait_Relighting_from_a_Single_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mei_Holo-Relighting_Controllable_Volumetric_CVPR_2024_supplemental.pdf | null |
Noisy One-point Homographies are Surprisingly Good | Yaqing Ding, Jonathan Astermark, Magnus Oskarsson, Viktor Larsson | Two-view homography estimation is a classic and fundamental problem in computer vision. While conceptually simple the problem quickly becomes challenging when multiple planes are visible in the image pair. Even with correct matches each individual plane (homography) might have a very low number of inliers when comparing to the set of all correspondences. In practice this requires a large number of RANSAC iterations to generate a good model hypothesis. The current state-of-the-art methods therefore seek to reduce the sample size from four point correspondences originally by including additional information such as keypoint orientation/angles or local affine information. In this work we continue in this direction and propose a novel one-point solver that leverages different approximate constraints derived from the same auxiliary information. In experiments we obtain state-of-the-art results with execution time speed-ups on large benchmark datasets and show that it is more beneficial for the solver to be sample efficient compared to generating more accurate homographies. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ding_Noisy_One-point_Homographies_are_Surprisingly_Good_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ding_Noisy_One-point_Homographies_are_Surprisingly_Good_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ding_Noisy_One-point_Homographies_are_Surprisingly_Good_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ding_Noisy_One-point_Homographies_CVPR_2024_supplemental.pdf | null |
PointInfinity: Resolution-Invariant Point Diffusion Models | Zixuan Huang, Justin Johnson, Shoubhik Debnath, James M. Rehg, Chao-Yuan Wu | We present PointInfinity an efficient family of point cloud diffusion models. Our core idea is to use a transformer-based architecture with a fixed-size resolution-invariant latent representation. This enables efficient training with low-resolution point clouds while allowing high-resolution point clouds to be generated during inference. More importantly we show that scaling the test-time resolution beyond the training resolution improves the fidelity of generated point clouds and surfaces. We analyze this phenomenon and draw a link to classifier-free guidance commonly used in diffusion models demonstrating that both allow trading off fidelity and variability during inference. Experiments on CO3D show that PointInfinity can efficiently generate high-resolution point clouds (up to 131k points 31 times more than Point-E) with state-of-the-art quality. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_PointInfinity_Resolution-Invariant_Point_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.03566 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_PointInfinity_Resolution-Invariant_Point_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_PointInfinity_Resolution-Invariant_Point_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | null | null |
Panacea: Panoramic and Controllable Video Generation for Autonomous Driving | Yuqing Wen, Yucheng Zhao, Yingfei Liu, Fan Jia, Yanhui Wang, Chong Luo, Chi Zhang, Tiancai Wang, Xiaoyan Sun, Xiangyu Zhang | The field of autonomous driving increasingly demands high-quality annotated training data. In this paper we propose Panacea an innovative approach to generate panoramic and controllable videos in driving scenarios capable of yielding an unlimited numbers of diverse annotated samples pivotal for autonomous driving advancements. Panacea addresses two critical challenges: 'Consistency' and 'Controllability.' Consistency ensures temporal and cross-view coherence while Controllability ensures the alignment of generated content with corresponding annotations. Our approach integrates a novel 4D attention and a two-stage generation pipeline to maintain coherence supplemented by the ControlNet framework for meticulous control by the Bird's-Eye-View (BEV) layouts. Extensive qualitative and quantitative evaluations of Panacea on the nuScenes dataset prove its effectiveness in generating high-quality multi-view driving-scene videos. This work notably propels the field of autonomous driving by effectively augmenting the training dataset used for advanced BEV perception techniques. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wen_Panacea_Panoramic_and_Controllable_Video_Generation_for_Autonomous_Driving_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16813 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wen_Panacea_Panoramic_and_Controllable_Video_Generation_for_Autonomous_Driving_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wen_Panacea_Panoramic_and_Controllable_Video_Generation_for_Autonomous_Driving_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wen_Panacea_Panoramic_and_CVPR_2024_supplemental.pdf | null |
Open-Vocabulary Semantic Segmentation with Image Embedding Balancing | Xiangheng Shan, Dongyue Wu, Guilin Zhu, Yuanjie Shao, Nong Sang, Changxin Gao | Open-vocabulary semantic segmentation is a challenging task which requires the model to output semantic masks of an image beyond a close-set vocabulary. Although many efforts have been made to utilize powerful CLIP models to accomplish this task they are still easily overfitting to training classes due to the natural gaps in semantic information between training and new classes. To overcome this challenge we propose a novel framework for open-vocabulary semantic segmentation called EBSeg incorporating an Adaptively Balanced Decoder (AdaB Decoder) and a Semantic Structure Consistency loss (SSC Loss). The AdaB Decoder is designed to generate different image embeddings for both training and new classes. Subsequently these two types of embeddings are adaptively balanced to fully exploit their ability to recognize training classes and generalization ability for new classes. To learn a consistent semantic structure from CLIP the SSC Loss aligns the inter-classes affinity in the image feature space with that in the text feature space of CLIP thereby improving the generalization ability of our model. Furthermore we employ a frozen SAM image encoder to complement the spatial information that CLIP features lack due to the low training image resolution and image-level supervision inherent in CLIP. Extensive experiments conducted across various benchmarks demonstrate that the proposed EBSeg outperforms the state-of-the-art methods. Our code and trained models will be here: https://github.com/slonetime/EBSeg. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shan_Open-Vocabulary_Semantic_Segmentation_with_Image_Embedding_Balancing_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shan_Open-Vocabulary_Semantic_Segmentation_with_Image_Embedding_Balancing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shan_Open-Vocabulary_Semantic_Segmentation_with_Image_Embedding_Balancing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shan_Open-Vocabulary_Semantic_Segmentation_CVPR_2024_supplemental.pdf | null |
Structured Model Probing: Empowering Efficient Transfer Learning by Structured Regularization | Zhi-Fan Wu, Chaojie Mao, Xue Wang, Jianwen Jiang, Yiliang Lv, Rong Jin | Despite encouraging results from recent developments in transfer learning for adapting pre-trained model to downstream tasks the performance of model probing is still lagging behind the state-of-the-art parameter efficient tuning methods. Our investigation reveals that existing model probing methods perform well for the easy case when the source domain (where models are pre-trained) and the adapted domain are similar but fail for the difficult case when the two domains are significantly different. Simply incorporating features extracted from multiple layers and increasing complexity of the probing model can mitigate the gap in the difficult case but degrades the performance in the easy case. To address this challenge we propose structured model probing (SMP) that is able to deliver good performance for both cases through structured regularization. The regularization performs feature selection leveraging model structure as a prior and controls the complexity of the probing model through the weights of selected structures. This enables us to construct a simple adaptation model with a small number of selected features and a linear prediction model for the easy case; and to automatically increase the complexity of adaptation model with a large number of selected features and a non-linear model for the difficult case. Our extensive empirical studies show that SMP significantly outperforms the state-of-the-art methods for parameter efficient tuning and at the same time still maintains the advantage of computational efficiency for probing-based methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Structured_Model_Probing_Empowering_Efficient_Transfer_Learning_by_Structured_Regularization_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Structured_Model_Probing_Empowering_Efficient_Transfer_Learning_by_Structured_Regularization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Structured_Model_Probing_Empowering_Efficient_Transfer_Learning_by_Structured_Regularization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_Structured_Model_Probing_CVPR_2024_supplemental.pdf | null |
Multi-Modal Proxy Learning Towards Personalized Visual Multiple Clustering | Jiawei Yao, Qi Qian, Juhua Hu | Multiple clustering has gained significant attention in recent years due to its potential to reveal multiple hidden structures of data from different perspectives. The advent of deep multiple clustering techniques has notably advanced the performance by uncovering complex patterns and relationships within large datasets. However a major challenge arises as users often do not need all the clusterings that algorithms generate and figuring out the one needed requires a substantial understanding of each clustering result. Traditionally aligning a user's brief keyword of interest with the corresponding vision components was challenging but the emergence of multi-modal and large language models (LLMs) has begun to bridge this gap. In response given unlabeled target visual data we propose Multi-Map a novel method employing a multi-modal proxy learning process. It leverages CLIP encoders to extract coherent text and image embeddings with GPT-4 integrating users' interests to formulate effective textual contexts. Moreover reference word constraint and concept-level constraint are designed to learn the optimal text proxy according to the user's interest. Multi-Map not only adeptly captures a user's interest via a keyword but also facilitates identifying relevant clusterings. Our extensive experiments show that Multi-Map consistently outperforms state-of-the-art methods in all benchmark multi-clustering vision tasks. Our code is available at https://github.com/Alexander-Yao/Multi-MaP. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yao_Multi-Modal_Proxy_Learning_Towards_Personalized_Visual_Multiple_Clustering_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.15655 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yao_Multi-Modal_Proxy_Learning_Towards_Personalized_Visual_Multiple_Clustering_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yao_Multi-Modal_Proxy_Learning_Towards_Personalized_Visual_Multiple_Clustering_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yao_Multi-Modal_Proxy_Learning_CVPR_2024_supplemental.pdf | null |
DreamMatcher: Appearance Matching Self-Attention for Semantically-Consistent Text-to-Image Personalization | Jisu Nam, Heesu Kim, DongJae Lee, Siyoon Jin, Seungryong Kim, Seunggyu Chang | The objective of text-to-image (T2I) personalization is to customize a diffusion model to a user-provided reference concept generating diverse images of the concept aligned with the target prompts. Conventional methods representing the reference concepts using unique text embeddings often fail to accurately mimic the appearance of the reference. To address this one solution may be explicitly conditioning the reference images into the target denoising process known as key-value replacement. However prior works are constrained to local editing since they disrupt the structure path of the pre-trained T2I model. To overcome this we propose a novel plug-in method called DreamMatcher which reformulates T2I personalization as semantic matching. Specifically DreamMatcher replaces the target values with reference values aligned by semantic matching while leaving the structure path unchanged to preserve the versatile capability of pre-trained T2I models for generating diverse structures. We also introduce a semantic-consistent masking strategy to isolate the personalized concept from irrelevant regions introduced by the target prompts. Compatible with existing T2I models DreamMatcher shows significant improvements in complex scenarios. Intensive analyses demonstrate the effectiveness of our approach. | https://openaccess.thecvf.com/content/CVPR2024/papers/Nam_DreamMatcher_Appearance_Matching_Self-Attention_for_Semantically-Consistent_Text-to-Image_Personalization_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.09812 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Nam_DreamMatcher_Appearance_Matching_Self-Attention_for_Semantically-Consistent_Text-to-Image_Personalization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Nam_DreamMatcher_Appearance_Matching_Self-Attention_for_Semantically-Consistent_Text-to-Image_Personalization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Nam_DreamMatcher_Appearance_Matching_CVPR_2024_supplemental.pdf | null |
Stronger Fewer & Superior: Harnessing Vision Foundation Models for Domain Generalized Semantic Segmentation | Zhixiang Wei, Lin Chen, Yi Jin, Xiaoxiao Ma, Tianle Liu, Pengyang Ling, Ben Wang, Huaian Chen, Jinjin Zheng | In this paper we first assess and harness various Vision Foundation Models (VFMs) in the context of Domain Generalized Semantic Segmentation (DGSS). Driven by the motivation that Leveraging Stronger pre-trained models and Fewer trainable parameters for Superior generalizability we introduce a robust fine-tuning approach namely "Rein" to parameter-efficiently harness VFMs for DGSS. Built upon a set of trainable tokens each linked to distinct instances Rein precisely refines and forwards the feature maps from each layer to the next layer within the backbone. This process produces diverse refinements for different categories within a single image. With fewer trainable parameters Rein efficiently fine-tunes VFMs for DGSS tasks surprisingly surpassing full parameter fine-tuning. Extensive experiments across various settings demonstrate that Rein significantly outperforms state-of-the-art methods. Remarkably with just an extra 1% of trainable parameters within the frozen backbone Rein achieves a mIoU of 68.1% on the Cityscapes without accessing any real urban-scene datasets. Code is available at https://github.com/w1oves/Rein.git. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wei_Stronger_Fewer__Superior_Harnessing_Vision_Foundation_Models_for_Domain_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.04265 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wei_Stronger_Fewer__Superior_Harnessing_Vision_Foundation_Models_for_Domain_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wei_Stronger_Fewer__Superior_Harnessing_Vision_Foundation_Models_for_Domain_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wei_Stronger_Fewer__CVPR_2024_supplemental.pdf | null |
PolarMatte: Fully Computational Ground-Truth-Quality Alpha Matte Extraction for Images and Video using Polarized Screen Matting | Kenji Enomoto, TJ Rhodes, Brian Price, Gavin Miller | The creation of high-quality alpha mattes as ground-truth data for video matting is typically a laborious task. The trade-off between accuracy manual corrections and capture constraints often produces erroneous results or is cost prohibitive. We propose PolarMatte a fully computational alpha matte extraction method for images and video without compromise between quality and practicality. A single polarization camera is used to capture dynamic scenes backlit by an off-the-shelf LCD monitor. PolarMatte exploits the polarization channel to compute the per-pixel opacity of the target scene including the transparency of fine-details translucent objects and optical/motion blur. We leverage polarization clues to robustly detect indistinguishable pixels and extract the alpha matte value at polarized foreground reflections with a polarimetric matting Laplacian. Quantitative and qualitative evaluation demonstrate our ability to computationally extract ground-truth-quality alpha mattes without human labour. | https://openaccess.thecvf.com/content/CVPR2024/papers/Enomoto_PolarMatte_Fully_Computational_Ground-Truth-Quality_Alpha_Matte_Extraction_for_Images_and_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Enomoto_PolarMatte_Fully_Computational_Ground-Truth-Quality_Alpha_Matte_Extraction_for_Images_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Enomoto_PolarMatte_Fully_Computational_Ground-Truth-Quality_Alpha_Matte_Extraction_for_Images_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Enomoto_PolarMatte_Fully_Computational_CVPR_2024_supplemental.zip | null |
ChAda-ViT : Channel Adaptive Attention for Joint Representation Learning of Heterogeneous Microscopy Images | Nicolas Bourriez, Ihab Bendidi, Ethan Cohen, Gabriel Watkinson, Maxime Sanchez, Guillaume Bollot, Auguste Genovesio | Unlike color photography images which are consistently encoded into RGB channels biological images encompass various modalities where the type of microscopy and the meaning of each channel varies with each experiment. Importantly the number of channels can range from one to a dozen and their correlation is often comparatively much lower than RGB as each of them brings specific information content. This aspect is largely overlooked by methods designed out of the bioimage field and current solutions mostly focus on intra-channel spatial attention often ignoring the relationship between channels yet crucial in most biological applications. Importantly the variable channel type and count prevent the projection of several experiments to a unified representation for large scale pre-training. In this study we propose ChAda-ViT a novel Channel Adaptive Vision Transformer architecture employing an Inter-Channel Attention mechanism on images with an arbitrary number order and type of channels. We also introduce IDRCell100k a bioimage dataset with a rich set of 79 experiments covering 7 microscope modalities with a multitude of channel types and channel counts varying from 1 to 10 per experiment. Our proposed architecture trained in a self-supervised manner outperforms existing approaches in several biologically relevant downstream tasks. Additionally it can be used to bridge the gap for the first time between assays with different microscopes channel numbers or types by embedding various image and experimental modalities into a unified biological image representation. The latter should facilitate interdisciplinary studies and pave the way for better adoption of deep learning in biological image-based analyses. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bourriez_ChAda-ViT__Channel_Adaptive_Attention_for_Joint_Representation_Learning_of_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.15264 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bourriez_ChAda-ViT__Channel_Adaptive_Attention_for_Joint_Representation_Learning_of_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bourriez_ChAda-ViT__Channel_Adaptive_Attention_for_Joint_Representation_Learning_of_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bourriez_ChAda-ViT__Channel_CVPR_2024_supplemental.pdf | null |
CARZero: Cross-Attention Alignment for Radiology Zero-Shot Classification | Haoran Lai, Qingsong Yao, Zihang Jiang, Rongsheng Wang, Zhiyang He, Xiaodong Tao, S. Kevin Zhou | The advancement of Zero-Shot Learning in the medical domain has been driven forward by using pre-trained models on large-scale image-text pairs focusing on image-text alignment. However existing methods primarily rely on cosine similarity for alignment which may not fully capture the complex relationship between medical images and reports. To address this gap we introduce a novel approach called Cross-Attention Alignment for Radiology Zero-Shot Classification (CARZero). Our approach innovatively leverages cross-attention mechanisms to process image and report features creating a Similarity Representation that more accurately reflects the intricate relationships in medical semantics. This representation is then linearly projected to form an image-text similarity matrix for cross-modality alignment. Additionally recognizing the pivotal role of prompt selection in zero-shot learning CARZero incorporates a Large Language Model-based prompt alignment strategy. This strategy standardizes diverse diagnostic expressions into a unified format for both training and inference phases overcoming the challenges of manual prompt design. Our approach is simple yet effective demonstrating state-of-the-art performance in zero-shot classification on five official chest radiograph diagnostic test sets including remarkable results on datasets with long-tail distributions of rare diseases. This achievement is attributed to our new image-text alignment strategy which effectively addresses the complex relationship between medical images and reports. Code and models are available at https://github.com/laihaoran/CARZero. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lai_CARZero_Cross-Attention_Alignment_for_Radiology_Zero-Shot_Classification_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.17417 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lai_CARZero_Cross-Attention_Alignment_for_Radiology_Zero-Shot_Classification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lai_CARZero_Cross-Attention_Alignment_for_Radiology_Zero-Shot_Classification_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lai_CARZero_Cross-Attention_Alignment_CVPR_2024_supplemental.pdf | null |
HOIDiffusion: Generating Realistic 3D Hand-Object Interaction Data | Mengqi Zhang, Yang Fu, Zheng Ding, Sifei Liu, Zhuowen Tu, Xiaolong Wang | 3D hand-object interaction data is scarce due to the hardware constraints in scaling up the data collection process. In this paper we propose HOIDiffusion for generating realistic and diverse 3D hand-object interaction data. Our model is a conditional diffusion model that takes both the 3D hand-object geometric structure and text description as inputs for image synthesis. This offers a more controllable and realistic synthesis as we can specify the structure and style inputs in a disentangled manner. HOIDiffusion is trained by leveraging a diffusion model pre-trained on large-scale natural images and a few 3D human demonstrations. Beyond controllable image synthesis we adopt the generated 3D data for learning 6D object pose estimation and show its effectiveness in improving perception systems. Project page: https://mq-zhang1.github.io/HOIDiffusion. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_HOIDiffusion_Generating_Realistic_3D_Hand-Object_Interaction_Data_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.12011 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_HOIDiffusion_Generating_Realistic_3D_Hand-Object_Interaction_Data_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_HOIDiffusion_Generating_Realistic_3D_Hand-Object_Interaction_Data_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_HOIDiffusion_Generating_Realistic_CVPR_2024_supplemental.pdf | null |
VecFusion: Vector Font Generation with Diffusion | Vikas Thamizharasan, Difan Liu, Shantanu Agarwal, Matthew Fisher, Michael Gharbi, Oliver Wang, Alec Jacobson, Evangelos Kalogerakis | We present VecFusion a new neural architecture that can generate vector fonts with varying topological structures and precise control point positions. Our approach is a cascaded diffusion model which consists of a raster diffusion model followed by a vector diffusion model. The raster model generates low-resolution rasterized fonts with auxiliary control point information capturing the global style and shape of the font while the vector model synthesizes vector fonts conditioned on the low-resolution raster fonts from the first stage. To synthesize long and complex curves our vector diffusion model uses a transformer architecture and a novel vector representation that enables the modeling of diverse vector geometry and the precise prediction of control points. Our experiments show that in contrast to previous generative models for vector graphics our new cascaded vector diffusion model generates higher quality vector fonts with complex structures and diverse styles. | https://openaccess.thecvf.com/content/CVPR2024/papers/Thamizharasan_VecFusion_Vector_Font_Generation_with_Diffusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.10540 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Thamizharasan_VecFusion_Vector_Font_Generation_with_Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Thamizharasan_VecFusion_Vector_Font_Generation_with_Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Thamizharasan_VecFusion_Vector_Font_CVPR_2024_supplemental.pdf | null |
Multi-Modal Hallucination Control by Visual Information Grounding | Alessandro Favero, Luca Zancato, Matthew Trager, Siddharth Choudhary, Pramuditha Perera, Alessandro Achille, Ashwin Swaminathan, Stefano Soatto | Generative Vision-Language Models (VLMs) are prone to generate plausible-sounding textual answers which however are not always grounded in the input image. We investigate this phenomenon usually referred to as "hallucination" and show that it stems from an excessive reliance on the language prior. In particular we show that as more tokens are generated the reliance on the visual prompt decreases and this behavior strongly correlates with the emergence of hallucinations. To reduce hallucinations we introduce Multi-Modal Mutual-Information Decoding (M3ID) a new sampling method for prompt amplification. M3ID amplifies the influence of the reference image over the language prior hence favoring the generation of tokens with higher mutual information with the visual prompt. M3ID can be applied to any pre-trained autoregressive VLM at inference time without necessitating further training and with minimal computational overhead. If training is an option we show that M3ID can be paired with Direct Preference Optimization (DPO) to improve the model's reliance on the prompt image without requiring any labels. Our empirical findings show that our algorithms maintain the fluency and linguistic capabilities of pre-trained VLMs while reducing hallucinations by mitigating visually ungrounded answers. Specifically for the LLaVA 13B model M3ID and M3ID+DPO reduce the percentage of hallucinated objects in captioning tasks by 25% and 28% respectively and improve the accuracy on VQA benchmarks such as POPE by 21% and 24%. | https://openaccess.thecvf.com/content/CVPR2024/papers/Favero_Multi-Modal_Hallucination_Control_by_Visual_Information_Grounding_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.14003 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Favero_Multi-Modal_Hallucination_Control_by_Visual_Information_Grounding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Favero_Multi-Modal_Hallucination_Control_by_Visual_Information_Grounding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Favero_Multi-Modal_Hallucination_Control_CVPR_2024_supplemental.pdf | null |
Towards Text-guided 3D Scene Composition | Qihang Zhang, Chaoyang Wang, Aliaksandr Siarohin, Peiye Zhuang, Yinghao Xu, Ceyuan Yang, Dahua Lin, Bolei Zhou, Sergey Tulyakov, Hsin-Ying Lee | We are witnessing significant breakthroughs in the technology for generating 3D objects from text. Existing approaches either leverage large text-to-image models to optimize a 3D representation or train 3D generators on object-centric datasets. Generating entire scenes however remains very challenging as a scene contains multiple 3D objects diverse and scattered. In this work we introduce SceneWiz3D - a novel approach to synthesize high-fidelity 3D scenes from text. We marry the locality of objects with globality of scenes by introducing a hybrid 3D representation - explicit for objects and implicit for scenes. Remarkably an object being represented explicitly can be either generated from text using conventional text-to-3D approaches or provided by users. To configure the layout of the scene and automatically place objects we apply the Particle Swarm Optimization technique during the optimization process. Furthermore it is difficult for certain parts of the scene (e.g. corners occlusion) to receive multi-view supervision leading to inferior geometry. We incorporate an RGBD panorama diffusion model to mitigate it resulting in high-quality geometry. Extensive evaluation supports that our approach achieves superior quality over previous approaches enabling the generation of detailed and view-consistent 3D scenes. Our project website is at https://zqh0253.github.io/SceneWiz3D.\\ | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Towards_Text-guided_3D_Scene_Composition_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.08885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Towards_Text-guided_3D_Scene_Composition_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Towards_Text-guided_3D_Scene_Composition_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Towards_Text-guided_3D_CVPR_2024_supplemental.pdf | null |
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture Modeling | Haiyang Liu, Zihao Zhu, Giorgio Becherini, Yichen Peng, Mingyang Su, You Zhou, Xuefei Zhe, Naoya Iwamoto, Bo Zheng, Michael J. Black | We propose EMAGE a framework to generate full-body human gestures from audio and masked gestures encompassing facial local body hands and global movements. To achieve this we first introduce BEAT2 (BEAT-SMPLX-FLAME) a new mesh-level holistic co-speech dataset. BEAT2 combines a MoShed SMPL-X body with FLAME head parameters and further refines the modeling of head neck and finger movements offering a community-standardized high-quality 3D motion captured dataset. EMAGE leverages masked body gesture priors during training to boost inference performance. It involves a Masked Audio Gesture Transformer facilitating joint training on audio-to-gesture generation and masked gesture reconstruction to effectively encode audio and body gesture hints. Encoded body hints from masked gestures are then separately employed to generate facial and body movements. Moreover EMAGE adaptively merges speech features from the audio's rhythm and content and utilizes four compositional VQ-VAEs to enhance the results' fidelity and diversity. Experiments demonstrate that EMAGE generates holistic gestures with state-of-the-art performance and is flexible in accepting predefined spatial-temporal gesture inputs generating complete audio-synchronized results. Our code and dataset are available. https://pantomatrix.github.io/EMAGE/ | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_EMAGE_Towards_Unified_Holistic_Co-Speech_Gesture_Generation_via_Expressive_Masked_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.00374 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_EMAGE_Towards_Unified_Holistic_Co-Speech_Gesture_Generation_via_Expressive_Masked_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_EMAGE_Towards_Unified_Holistic_Co-Speech_Gesture_Generation_via_Expressive_Masked_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_EMAGE_Towards_Unified_CVPR_2024_supplemental.pdf | null |
Adversarial Text to Continuous Image Generation | Kilichbek Haydarov, Aashiq Muhamed, Xiaoqian Shen, Jovana Lazarevic, Ivan Skorokhodov, Chamuditha Jayanga Galappaththige, Mohamed Elhoseiny | Existing GAN-based text-to-image models treat images as 2D pixel arrays. In this paper we approach the text-to-image task from a different perspective where a 2D image is represented as an implicit neural representation (INR). We show that straightforward conditioning of the unconditional INR-based GAN method on text inputs is not enough to achieve good performance. We propose a word-level attention-based weight modulation operator that controls the generation process of INR-GAN based on hypernetworks. Our experiments on benchmark datasets show that HyperCGAN achieves competitive performance to existing pixel-based methods and retains the properties of continuous generative models. | https://openaccess.thecvf.com/content/CVPR2024/papers/Haydarov_Adversarial_Text_to_Continuous_Image_Generation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Haydarov_Adversarial_Text_to_Continuous_Image_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Haydarov_Adversarial_Text_to_Continuous_Image_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Haydarov_Adversarial_Text_to_CVPR_2024_supplemental.pdf | null |
The Neglected Tails in Vision-Language Models | Shubham Parashar, Zhiqiu Lin, Tian Liu, Xiangjue Dong, Yanan Li, Deva Ramanan, James Caverlee, Shu Kong | Vision-language models (VLMs) excel in zero-shot recognition but their performance varies greatly across different visual concepts. For example although CLIP achieves impressive accuracy on ImageNet (60-80%) its performance drops below 10% for more than ten concepts like night snake presumably due to their limited presence in the pretraining data. However measuring the frequency of concepts in VLMs' large-scale datasets is challenging. We address this by using large language models (LLMs) to count the number of pretraining texts that contain synonyms of these concepts. Our analysis confirms that popular datasets such as LAION exhibit a long-tailed concept distribution yielding biased performance in VLMs. We also find that downstream applications of VLMs including visual chatbots (e.g. GPT-4V) and text-to-image models (e.g. Stable Diffusion) often fail to recognize or generate images of rare concepts identified by our method. To mitigate the imbalanced performance of zero-shot VLMs we propose REtrieval-Augmented Learning (REAL). First instead of prompting VLMs using the original class names REAL uses their most frequent synonyms found in pretraining texts. This simple change already outperforms costly human-engineered and LLM-enriched prompts over nine benchmark datasets. Second REAL trains a linear classifier on a small yet balanced set of pretraining data retrieved using concept synonyms. REAL surpasses the previous zero-shot SOTA using 400x less storage and 10000x less training time! | https://openaccess.thecvf.com/content/CVPR2024/papers/Parashar_The_Neglected_Tails_in_Vision-Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.12425 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Parashar_The_Neglected_Tails_in_Vision-Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Parashar_The_Neglected_Tails_in_Vision-Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Parashar_The_Neglected_Tails_CVPR_2024_supplemental.pdf | null |
Learning Background Prompts to Discover Implicit Knowledge for Open Vocabulary Object Detection | Jiaming Li, Jiacheng Zhang, Jichang Li, Ge Li, Si Liu, Liang Lin, Guanbin Li | Open vocabulary object detection (OVD) aims at seeking an optimal object detector capable of recognizing objects from both base and novel categories. Recent advances leverage knowledge distillation to transfer insightful knowledge from pre-trained large-scale vision-language models to the task of object detection significantly generalizing the powerful capabilities of the detector to identify more unknown object categories. However these methods face significant challenges in background interpretation and model overfitting and thus often result in the loss of crucial background knowledge giving rise to sub-optimal inference performance of the detector. To mitigate these issues we present a novel OVD framework termed LBP to propose learning background prompts to harness explored implicit background knowledge thus enhancing the detection performance w.r.t. base and novel categories. Specifically we devise three modules: Background Category-specific Prompt Background Object Discovery and Inference Probability Rectification to empower the detector to discover represent and leverage implicit object knowledge explored from background proposals. Evaluation on two benchmark datasets OV-COCO and OV-LVIS demonstrates the superiority of our proposed method over existing state-of-the-art approaches in handling the OVD tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Learning_Background_Prompts_to_Discover_Implicit_Knowledge_for_Open_Vocabulary_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Learning_Background_Prompts_to_Discover_Implicit_Knowledge_for_Open_Vocabulary_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Learning_Background_Prompts_to_Discover_Implicit_Knowledge_for_Open_Vocabulary_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Learning_Background_Prompts_CVPR_2024_supplemental.pdf | null |
HumanNeRF-SE: A Simple yet Effective Approach to Animate HumanNeRF with Diverse Poses | Caoyuan Ma, Yu-Lun Liu, Zhixiang Wang, Wu Liu, Xinchen Liu, Zheng Wang | We present HumanNeRF-SE a simple yet effective method that synthesizes diverse novel pose images with simple input. Previous HumanNeRF works require a large number of optimizable parameters to fit the human images. Instead we reload these approaches by combining explicit and implicit human representations to design both generalized rigid deformation and specific non-rigid deformation. Our key insight is that explicit shape can reduce the sampling points used to fit implicit representation and frozen blending weights from SMPL constructing a generalized rigid deformation can effectively avoid overfitting and improve pose generalization performance. Our architecture involving both explicit and implicit representation is simple yet effective. Experiments demonstrate our model can synthesize images under arbitrary poses with few-shot input and increase the speed of synthesizing images by 15 times through a reduction in computational complexity without using any existing acceleration modules. Compared to the state-of-the-art HumanNeRF studies HumanNeRF-SE achieves better performance with fewer learnable parameters and less training time. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ma_HumanNeRF-SE_A_Simple_yet_Effective_Approach_to_Animate_HumanNeRF_with_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_HumanNeRF-SE_A_Simple_yet_Effective_Approach_to_Animate_HumanNeRF_with_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ma_HumanNeRF-SE_A_Simple_yet_Effective_Approach_to_Animate_HumanNeRF_with_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ma_HumanNeRF-SE_A_Simple_CVPR_2024_supplemental.pdf | null |
HOLD: Category-agnostic 3D Reconstruction of Interacting Hands and Objects from Video | Zicong Fan, Maria Parelli, Maria Eleni Kadoglou, Xu Chen, Muhammed Kocabas, Michael J. Black, Otmar Hilliges | Since humans interact with diverse objects every day the holistic 3D capture of these interactions is important to understand and model human behaviour. However most existing methods for hand-object reconstruction from RGB either assume pre-scanned object templates or heavily rely on limited 3D hand-object data restricting their ability to scale and generalize to more unconstrained interaction settings. To address this we introduce HOLD -- the first category-agnostic method that reconstructs an articulated hand and an object jointly from a monocular interaction video. We develop a compositional articulated implicit model that can reconstruct disentangled 3D hands and objects from 2D images. We also further incorporate hand-object constraints to improve hand-object poses and consequently the reconstruction quality. Our method does not rely on any 3D hand-object annotations while significantly outperforming fully-supervised baselines in both in-the-lab and challenging in-the-wild settings. Moreover we qualitatively show its robustness in reconstructing from in-the-wild videos. See https://github.com/zc-alexfan/hold for code data models and updates. | https://openaccess.thecvf.com/content/CVPR2024/papers/Fan_HOLD_Category-agnostic_3D_Reconstruction_of_Interacting_Hands_and_Objects_from_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18448 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_HOLD_Category-agnostic_3D_Reconstruction_of_Interacting_Hands_and_Objects_from_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Fan_HOLD_Category-agnostic_3D_Reconstruction_of_Interacting_Hands_and_Objects_from_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Fan_HOLD_Category-agnostic_3D_CVPR_2024_supplemental.pdf | null |
Continual Segmentation with Disentangled Objectness Learning and Class Recognition | Yizheng Gong, Siyue Yu, Xiaoyang Wang, Jimin Xiao | Most continual segmentation methods tackle the problem as a per-pixel classification task. However such a paradigm is very challenging and we find query-based segmenters with built-in objectness have inherent advantages compared with per-pixel ones as objectness has strong transfer ability and forgetting resistance. Based on these findings we propose CoMasTRe by disentangling continual segmentation into two stages: forgetting-resistant continual objectness learning and well-researched continual classification. CoMasTRe uses a two-stage segmenter learning class-agnostic mask proposals at the first stage and leaving recognition to the second stage. During continual learning a simple but effective distillation is adopted to strengthen objectness. To further mitigate the forgetting of old classes we design a multi-label class distillation strategy suited for segmentation. We assess the effectiveness of CoMasTRe on PASCAL VOC and ADE20K. Extensive experiments show that our method outperforms per-pixel and query-based methods on both datasets. Code will be available at https://github.com/jordangong/CoMasTRe. | https://openaccess.thecvf.com/content/CVPR2024/papers/Gong_Continual_Segmentation_with_Disentangled_Objectness_Learning_and_Class_Recognition_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.03477 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gong_Continual_Segmentation_with_Disentangled_Objectness_Learning_and_Class_Recognition_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gong_Continual_Segmentation_with_Disentangled_Objectness_Learning_and_Class_Recognition_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gong_Continual_Segmentation_with_CVPR_2024_supplemental.pdf | null |
Towards Accurate Post-training Quantization for Diffusion Models | Changyuan Wang, Ziwei Wang, Xiuwei Xu, Yansong Tang, Jie Zhou, Jiwen Lu | In this paper we propose an accurate post-training quantization framework of diffusion models (APQ-DM) for efficient image generation. Conventional quantization frameworks learn shared quantization functions for tensor discretization regardless of the generation timesteps in diffusion models while the activation distribution differs significantly across various timesteps. Meanwhile the calibration images are acquired in random timesteps which fail to provide sufficient information for generalizable quantization function learning. Both issues cause sizable quantization errors with obvious image generation performance degradation. On the contrary we design distribution-aware quantization functions for activation discretization in different timesteps and search the optimal timesteps for informative calibration image generation so that our quantized diffusion model can reduce the discretization errors with negligible computational overhead. Specifically we partition various timestep quantization functions into different groups according to the importance weights which are optimized by differentiable search algorithms. We also extend structural risk minimization principle for informative calibration image generation to enhance the generalization ability in the deployment of quantized diffusion model. Extensive experimental results show that our method outperforms the state-of-the-art post-training quantization of diffusion model by a sizable margin with similar computational cost. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Towards_Accurate_Post-training_Quantization_for_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2305.18723 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Towards_Accurate_Post-training_Quantization_for_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Towards_Accurate_Post-training_Quantization_for_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Towards_Accurate_Post-training_CVPR_2024_supplemental.pdf | null |
ASAM: Boosting Segment Anything Model with Adversarial Tuning | Bo Li, Haoke Xiao, Lv Tang | In the evolving landscape of computer vision foundation models have emerged as pivotal tools exhibiting exceptional adaptability to a myriad of tasks. Among these the Segment Anything Model (SAM) by Meta AI has distinguished itself in image segmentation. However SAM like its counterparts encounters limitations in specific niche applications prompting a quest for enhancement strategies that do not compromise its inherent capabilities. This paper introduces ASAM a novel methodology that amplifies SAM's performance through adversarial tuning. We harness the potential of natural adversarial examples inspired by their successful implementation in natural language processing. By utilizing a stable diffusion model we augment a subset (1%) of the SA-1B dataset generating adversarial instances that are more representative of natural variations rather than conventional imperceptible perturbations. Our approach maintains the photorealism of adversarial examples and ensures alignment with original mask annotations thereby preserving the integrity of the segmentation task. The fine-tuned ASAM demonstrates significant improvements across a diverse range of segmentation tasks without necessitating additional data or architectural modifications. The results of our extensive evaluations confirm that ASAM establishes new benchmarks in segmentation tasks thereby contributing to the advancement of foundational models in computer vision. Our project page is in https://asam2024.github.io/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_ASAM_Boosting_Segment_Anything_Model_with_Adversarial_Tuning_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.00256 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_ASAM_Boosting_Segment_Anything_Model_with_Adversarial_Tuning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_ASAM_Boosting_Segment_Anything_Model_with_Adversarial_Tuning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_ASAM_Boosting_Segment_CVPR_2024_supplemental.pdf | null |
UniBind: LLM-Augmented Unified and Balanced Representation Space to Bind Them All | Yuanhuiyi Lyu, Xu Zheng, Jiazhou Zhou, Lin Wang | We present UniBind a flexible and efficient approach that learns a unified representation space for seven diverse modalities-- images text audio point cloud thermal video and event data. Existing works eg. ImageBind treat the image as the central modality and build an image-centered representation space; however the space may be sub-optimal as it leads to an unbalanced representation space among all modalities. Moreover the category names are directly used to extract text embeddings for the downstream tasks making it hardly possible to represent the semantics of multi-modal data. The 'out-of-the-box' insight of our UniBind is to make the alignment center modality-agnostic and further learn a unified and balanced representation space empowered by the large language models (LLMs). UniBind is superior in its flexible application to all CLIP-style models and delivers remarkable performance boosts. To make this possible we 1) construct a knowledge base of text embeddings with the help of LLMs and multi-modal LLMs; 2) adaptively build LLM-augmented class-wise embedding center on top of the knowledge base and encoded visual embeddings; 3) align all the embeddings to the LLM-augmented embedding center via contrastive learning to achieve a unified and balanced representation space. UniBind shows strong zero-shot recognition performance gains over prior arts by an average of 6.36%. Finally we achieve new state-of-the-art performance eg. a 6.75% gain on ImageNet on the multi-modal fine-tuning setting while reducing 90% of the learnable parameters. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lyu_UniBind_LLM-Augmented_Unified_and_Balanced_Representation_Space_to_Bind_Them_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.12532 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lyu_UniBind_LLM-Augmented_Unified_and_Balanced_Representation_Space_to_Bind_Them_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lyu_UniBind_LLM-Augmented_Unified_and_Balanced_Representation_Space_to_Bind_Them_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lyu_UniBind_LLM-Augmented_Unified_CVPR_2024_supplemental.pdf | null |
Dynamic Support Information Mining for Category-Agnostic Pose Estimation | Pengfei Ren, Yuanyuan Gao, Haifeng Sun, Qi Qi, Jingyu Wang, Jianxin Liao | Category-agnostic pose estimation (CAPE) aims to predict the pose of a query image based on few support images with pose annotations. Existing methods achieve the localization of arbitrary keypoints through similarity matching between support keypoint features and query image features. However these methods primarily focus on mining information from the query images neglecting the fact that support samples with keypoint annotations contain rich category-specific fine-grained semantic information and prior structural information. In this paper we propose a Support-based Dynamic Perception Network (SDPNet) for the robust and accurate CAPE. On the one hand SDPNet models complex dependencies between support keypoints constructing category-specific prior structure to guide the interaction of query keypoints. On the other hand SDPNet extracts fine-grained semantic information from support samples dynamically modulating the refinement process of query. Our method outperforms existing methods on MP-100 dataset by a large margin. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ren_Dynamic_Support_Information_Mining_for_Category-Agnostic_Pose_Estimation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ren_Dynamic_Support_Information_Mining_for_Category-Agnostic_Pose_Estimation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ren_Dynamic_Support_Information_Mining_for_Category-Agnostic_Pose_Estimation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ren_Dynamic_Support_Information_CVPR_2024_supplemental.pdf | null |
Test-Time Adaptation for Depth Completion | Hyoungseob Park, Anjali Gupta, Alex Wong | It is common to observe performance degradation when transferring models trained on some (source) datasets to target testing data due to a domain gap between them. Existing methods for bridging this gap such as domain adaptation (DA) may require the source data on which the model was trained (often not available) while others i.e. source-free DA require many passes through the testing data. We propose an online test-time adaptation method for depth completion the task of inferring a dense depth map from a single image and associated sparse depth map that closes the performance gap in a single pass. We first present a study on how the domain shift in each data modality affects model performance. Based on our observations that the sparse depth modality exhibits a much smaller covariate shift than the image we design an embedding module trained in the source domain that preserves a mapping from features encoding only sparse depth to those encoding image and sparse depth. During test time sparse depth features are projected using this map as a proxy for source domain features and are used as guidance to train a set of auxiliary parameters (i.e. adaptation layer) to align image and sparse depth features from the target test domain to that of the source domain. We evaluate our method on indoor and outdoor scenarios and show that it improves over baselines by an average of 21.1%. Code available at https://github.com/seobbro/TTA-depth-completion. | https://openaccess.thecvf.com/content/CVPR2024/papers/Park_Test-Time_Adaptation_for_Depth_Completion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.03312 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Park_Test-Time_Adaptation_for_Depth_Completion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Park_Test-Time_Adaptation_for_Depth_Completion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Park_Test-Time_Adaptation_for_CVPR_2024_supplemental.pdf | null |
GOAT-Bench: A Benchmark for Multi-Modal Lifelong Navigation | Mukul Khanna, Ram Ramrakhya, Gunjan Chhablani, Sriram Yenamandra, Theophile Gervet, Matthew Chang, Zsolt Kira, Devendra Singh Chaplot, Dhruv Batra, Roozbeh Mottaghi | The Embodied AI community has recently made significant strides in visual navigation tasks exploring targets from 3D coordinates objects language description and images. However these navigation models often handle only a single input modality as the target. With the progress achieved so far it is time to move towards universal navigation models capable of handling various goal types enabling more effective user interaction with robots. To facilitate this goal we propose GOAT-Bench a benchmark for the universal navigation task referred to as GO to AnyThing (GOAT). In this task the agent is directed to navigate to a sequence of targets specified by the category name language description or instance image in an open-vocabulary fashion. We benchmark monolithic RL and modular methods on the GOAT task analyzing their performance across modalities the role of explicit and implicit scene memories their robustness to noise in goal specifications and the impact of memory in lifelong scenarios. | https://openaccess.thecvf.com/content/CVPR2024/papers/Khanna_GOAT-Bench_A_Benchmark_for_Multi-Modal_Lifelong_Navigation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Khanna_GOAT-Bench_A_Benchmark_for_Multi-Modal_Lifelong_Navigation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Khanna_GOAT-Bench_A_Benchmark_for_Multi-Modal_Lifelong_Navigation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Khanna_GOAT-Bench_A_Benchmark_CVPR_2024_supplemental.pdf | null |
Taming Mode Collapse in Score Distillation for Text-to-3D Generation | Peihao Wang, Dejia Xu, Zhiwen Fan, Dilin Wang, Sreyas Mohan, Forrest Iandola, Rakesh Ranjan, Yilei Li, Qiang Liu, Zhangyang Wang, Vikas Chandra | Despite the remarkable performance of score distillation in text-to-3D generation such techniques notoriously suffer from view inconsistency issues also known as "Janus" artifact where the generated objects fake each view with multiple front faces. Although empirically effective methods have approached this problem via score debiasing or prompt engineering a more rigorous perspective to explain and tackle this problem remains elusive. In this paper we reveal that the existing score distillation-based text-to-3D generation frameworks degenerate to maximal likelihood seeking on each view independently and thus suffer from the mode collapse problem manifesting as the Janus artifact in practice. To tame mode collapse we improve score distillation by re-establishing the entropy term in the corresponding variational objective which is applied to the distribution of rendered images. Maximizing the entropy encourages diversity among different views in generated 3D assets thereby mitigating the Janus problem. Based on this new objective we derive a new update rule for 3D score distillation dubbed Entropic Score Distillation (ESD). We theoretically reveal that ESD can be simplified and implemented by just adopting the classifier-free guidance trick upon variational score distillation. Although embarrassingly straightforward our extensive experiments demonstrate that ESD can be an effective treatment for Janus artifacts in score distillation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Taming_Mode_Collapse_in_Score_Distillation_for_Text-to-3D_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.00909 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Taming_Mode_Collapse_in_Score_Distillation_for_Text-to-3D_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Taming_Mode_Collapse_in_Score_Distillation_for_Text-to-3D_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Taming_Mode_Collapse_CVPR_2024_supplemental.zip | null |
Binarized Low-light Raw Video Enhancement | Gengchen Zhang, Yulun Zhang, Xin Yuan, Ying Fu | Recently deep neural networks have achieved excellent performance on low-light raw video enhancement. However they often come with high computational complexity and large memory costs which hinder their applications on resource-limited devices. In this paper we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-light raw video enhancement. Nevertheless there are two main issues with binarizing video enhancement models. One is how to fuse the temporal information to improve low-light denoising without complex modules. The other is how to narrow the performance gap between binary convolutions with the full precision ones. To address the first issue we introduce a spatial-temporal shift operation which is easy-to-binarize and effective. The temporal shift efficiently aggregates the features of neighbor frames and the spatial shift handles the misalignment caused by the large motion in videos. For the second issue we present a distribution-aware binary convolution which captures the distribution characteristics of real-valued input and incorporates them into plain binary convolutions to alleviate the degradation in performance. Extensive quantitative and qualitative experiments have shown our high-efficiency binarized low-light raw video enhancement method can attain a promising performance. The code is available at https://github.com/ying-fu/BRVE. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Binarized_Low-light_Raw_Video_Enhancement_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Binarized_Low-light_Raw_Video_Enhancement_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Binarized_Low-light_Raw_Video_Enhancement_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Binarized_Low-light_Raw_CVPR_2024_supplemental.pdf | null |
MorpheuS: Neural Dynamic 360deg Surface Reconstruction from Monocular RGB-D Video | Hengyi Wang, Jingwen Wang, Lourdes Agapito | Neural rendering has demonstrated remarkable success in dynamic scene reconstruction. Thanks to the expressiveness of neural representations prior works can accurately capture the motion and achieve high-fidelity reconstruction of the target object. Despite this real-world video scenarios often feature large unobserved regions where neural representations struggle to achieve realistic completion. To tackle this challenge we introduce MorpheuS a framework for dynamic 360deg surface reconstruction from a casually captured RGB-D video. Our approach models the target scene as a canonical field that encodes its geometry and appearance in conjunction with a deformation field that warps points from the current frame to the canonical space. We leverage a view-dependent diffusion prior and distill knowledge from it to achieve realistic completion of unobserved regions. Experimental results on various real-world and synthetic datasets show that our method can achieve high-fidelity 360deg surface reconstruction of a deformable object from a monocular RGB-D video. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_MorpheuS_Neural_Dynamic_360deg_Surface_Reconstruction_from_Monocular_RGB-D_Video_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_MorpheuS_Neural_Dynamic_360deg_Surface_Reconstruction_from_Monocular_RGB-D_Video_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_MorpheuS_Neural_Dynamic_360deg_Surface_Reconstruction_from_Monocular_RGB-D_Video_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_MorpheuS_Neural_Dynamic_CVPR_2024_supplemental.pdf | null |
Decoupling Static and Hierarchical Motion Perception for Referring Video Segmentation | Shuting He, Henghui Ding | Referring video segmentation relies on natural language expressions to identify and segment objects often emphasizing motion clues. Previous works treat a sentence as a whole and directly perform identification at the video-level mixing up static image-level cues with temporal motion cues. However image-level features cannot well comprehend motion cues in sentences and static cues are not crucial for temporal perception. In fact static cues can sometimes interfere with temporal perception by overshadowing motion cues. In this work we propose to decouple video-level referring expression understanding into static and motion perception with a specific emphasis on enhancing temporal comprehension. Firstly we introduce an expression-decoupling module to make static cues and motion cues perform their distinct role alleviating the issue of sentence embeddings overlooking motion cues. Secondly we propose a hierarchical motion perception module to capture temporal information effectively across varying timescales. Furthermore we employ contrastive learning to distinguish the motions of visually similar objects. These contributions yield state-of-the-art performance across five datasets including a remarkable 9.2% J&F improvement on the challenging MeViS dataset. | https://openaccess.thecvf.com/content/CVPR2024/papers/He_Decoupling_Static_and_Hierarchical_Motion_Perception_for_Referring_Video_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.03645 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/He_Decoupling_Static_and_Hierarchical_Motion_Perception_for_Referring_Video_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/He_Decoupling_Static_and_Hierarchical_Motion_Perception_for_Referring_Video_Segmentation_CVPR_2024_paper.html | CVPR 2024 | null | null |
MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Model | Zhongcong Xu, Jianfeng Zhang, Jun Hao Liew, Hanshu Yan, Jia-Wei Liu, Chenxu Zhang, Jiashi Feng, Mike Zheng Shou | This paper studies the human image animation task which aims to generate a video of a certain reference identity following a particular motion sequence. Existing animation works typically employ the frame-warping technique to animate the reference image towards the target motion. Despite achieving reasonable results these approaches face challenges in maintaining temporal consistency throughout the animation due to the lack of temporal modeling and poor preservation of reference identity. In this work we introduce MagicAnimate a diffusion-based framework that aims at enhancing temporal consistency preserving reference image faithfully and improving animation fidelity. To achieve this we first develop a video diffusion model to encode temporal information. Second to maintain the appearance coherence across frames we introduce a novel appearance encoder to retain the intricate details of the reference image. Leveraging these two innovations we further employ a simple video fusion technique to encourage smooth transitions for long video animation. Empirical results demonstrate the superiority of our method over baseline approaches on two benchmarks. Notably our approach outperforms the strongest baseline by over 38% in terms of video fidelity on the challenging TikTok dancing dataset. Code and model will be made available at https://showlab.github.io/magicanimate. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_MagicAnimate_Temporally_Consistent_Human_Image_Animation_using_Diffusion_Model_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.16498 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_MagicAnimate_Temporally_Consistent_Human_Image_Animation_using_Diffusion_Model_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_MagicAnimate_Temporally_Consistent_Human_Image_Animation_using_Diffusion_Model_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_MagicAnimate_Temporally_Consistent_CVPR_2024_supplemental.pdf | null |
Dense Vision Transformer Compression with Few Samples | Hanxiao Zhang, Yifan Zhou, Guo-Hua Wang | Few-shot model compression aims to compress a large model into a more compact one with only a tiny training set (even without labels). Block-level pruning has recently emerged as a leading technique in achieving high accuracy and low latency in few-shot CNN compression. But few-shot compression for Vision Transformers (ViT) remains largely unexplored which presents a new challenge. In particular the issue of sparse compression exists in traditional CNN few-shot methods which can only produce very few compressed models of different model sizes. This paper proposes a novel framework for few-shot ViT compression named DC-ViT. Instead of dropping the entire block DC-ViT selectively eliminates the attention module while retaining and reusing portions of the MLP module. DC-ViT enables dense compression which outputs numerous compressed models that densely populate the range of model complexity. DC-ViT outperforms state-of-the-art few-shot compression methods by a significant margin of 10 percentage points along with lower latency in the compression of ViT and its variants. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Dense_Vision_Transformer_Compression_with_Few_Samples_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18708 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Dense_Vision_Transformer_Compression_with_Few_Samples_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Dense_Vision_Transformer_Compression_with_Few_Samples_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Dense_Vision_Transformer_CVPR_2024_supplemental.pdf | null |
Masked AutoDecoder is Effective Multi-Task Vision Generalist | Han Qiu, Jiaxing Huang, Peng Gao, Lewei Lu, Xiaoqin Zhang, Shijian Lu | Inspired by the success of general-purpose models in NLP recent studies attempt to unify different vision tasks in the same sequence format and employ autoregressive Transformers for sequence prediction. They apply uni-directional attention to capture sequential dependencies and generate task sequences recursively. However such autoregressive Transformers may not fit vision tasks well as vision task sequences usually lack the sequential dependencies typically observed in natural languages. In this work we design Masked AutoDecoder (MAD) an effective multi-task vision generalist. MAD consists of two core designs. First we develop a parallel decoding framework that introduces bi-directional attention to capture contextual dependencies comprehensively and decode vision task sequences in parallel. Second we design a masked sequence modeling approach that learns rich task contexts by masking and reconstructing task sequences. In this way MAD handles all the tasks by a single network branch and a simple cross-entropy loss with minimal task-specific designs. Extensive experiments demonstrate the great potential of MAD as a new paradigm for unifying various vision tasks. MAD achieves superior performance and inference efficiency compared to autoregressive counterparts while obtaining competitive accuracy with task-specific models. Code will be released at https://github.com/hanqiu-hq/MAD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qiu_Masked_AutoDecoder_is_Effective_Multi-Task_Vision_Generalist_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.07692 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qiu_Masked_AutoDecoder_is_Effective_Multi-Task_Vision_Generalist_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qiu_Masked_AutoDecoder_is_Effective_Multi-Task_Vision_Generalist_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qiu_Masked_AutoDecoder_is_CVPR_2024_supplemental.pdf | null |
Weakly Misalignment-free Adaptive Feature Alignment for UAVs-based Multimodal Object Detection | Chen Chen, Jiahao Qi, Xingyue Liu, Kangcheng Bin, Ruigang Fu, Xikun Hu, Ping Zhong | Visible-infrared (RGB-IR) image fusion has shown great potentials in object detection based on unmanned aerial vehicles (UAVs). However the weakly misalignment problem between multimodal image pairs limits its performance in object detection. Most existing methods often ignore the modality gap and emphasize a strict alignment resulting in an upper bound of alignment quality and an increase of implementation costs. To address these challenges we propose a novel method named Offset-guided Adaptive Feature Alignment (OAFA) which could adaptively adjust the relative positions between multimodal features. Considering the impact of modality gap on the cross-modality spatial matching a Cross-modality Spatial Offset Modeling (CSOM) module is designed to establish a common subspace to estimate the precise feature-level offsets. Then an Offset-guided Deformable Alignment and Fusion (ODAF) module is utilized to implicitly capture optimal fusion positions for detection task rather than conducting a strict alignment. Comprehensive experiments demonstrate that our method not only achieves state-of-the-art performance in the UAVs-based object detection task but also shows strong robustness to the weakly misalignment problem. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Weakly_Misalignment-free_Adaptive_Feature_Alignment_for_UAVs-based_Multimodal_Object_Detection_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Weakly_Misalignment-free_Adaptive_Feature_Alignment_for_UAVs-based_Multimodal_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Weakly_Misalignment-free_Adaptive_Feature_Alignment_for_UAVs-based_Multimodal_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Weakly_Misalignment-free_Adaptive_CVPR_2024_supplemental.pdf | null |
From Correspondences to Pose: Non-minimal Certifiably Optimal Relative Pose without Disambiguation | Javier Tirado-Garín, Javier Civera | Estimating the relative camera pose from n \geq 5 correspondences between two calibrated views is a fundamental task in computer vision. This process typically involves two stages: 1) estimating the essential matrix between the views and 2) disambiguating among the four candidate relative poses that satisfy the epipolar geometry. In this paper we demonstrate a novel approach that for the first time bypasses the second stage. Specifically we show that it is possible to directly estimate the correct relative camera pose from correspondences without needing a post-processing step to enforce the cheirality constraint on the correspondences. Building on recent advances in certifiable non-minimal optimization we frame the relative pose estimation as a Quadratically Constrained Quadratic Program (QCQP). By applying the appropriate constraints we ensure the estimation of a camera pose that corresponds to a valid 3D geometry and that is globally optimal when certified. We validate our method through exhaustive synthetic and real-world experiments confirming the efficacy efficiency and accuracy of the proposed approach. Code is available at https://github.com/javrtg/C2P. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tirado-Garin_From_Correspondences_to_Pose_Non-minimal_Certifiably_Optimal_Relative_Pose_without_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tirado-Garin_From_Correspondences_to_Pose_Non-minimal_Certifiably_Optimal_Relative_Pose_without_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tirado-Garin_From_Correspondences_to_Pose_Non-minimal_Certifiably_Optimal_Relative_Pose_without_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tirado-Garin_From_Correspondences_to_CVPR_2024_supplemental.pdf | null |
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging | Bhargav Ghanekar, Salman Siddique Khan, Pranav Sharma, Shreyas Singh, Vivek Boominathan, Kaushik Mitra, Ashok Veeraraghavan | Passive compact single-shot 3D sensing is useful in many application areas such as microscopy medical imaging surgical navigation and autonomous driving where form factor time and power constraints can exist. Obtaining RGB-D scene information over a short imaging distance in an ultra-compact form factor and in a passive snapshot manner is challenging. Dual-pixel (DP) sensors are a potential solution to achieve the same. DP sensors collect light rays from two different halves of the lens in two interleaved pixel arrays thus capturing two slightly different views of the scene like a stereo camera system. However imaging with a DP sensor implies that the defocus blur size is directly proportional to the disparity seen between the views. This creates a trade-off between disparity estimation vs. deblurring accuracy. To improve this trade-off effect we propose CADS (Coded Aperture Dual-Pixel Sensing) in which we use a coded aperture in the imaging lens along with a DP sensor. In our approach we jointly learn an optimal coded pattern and the reconstruction algorithm in an end-to-end optimization setting. Our resulting CADS imaging system demonstrates improvement of >1.5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings. Furthermore we build the proposed CADS prototypes for DSLR photography settings and in an endoscope and a dermoscope form factor. Our novel coded dual-pixel sensing approach demonstrates accurate RGB-D reconstruction results in simulations and real-world experiments in a passive snapshot and compact manner. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ghanekar_Passive_Snapshot_Coded_Aperture_Dual-Pixel_RGB-D_Imaging_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ghanekar_Passive_Snapshot_Coded_Aperture_Dual-Pixel_RGB-D_Imaging_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ghanekar_Passive_Snapshot_Coded_Aperture_Dual-Pixel_RGB-D_Imaging_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ghanekar_Passive_Snapshot_Coded_CVPR_2024_supplemental.pdf | null |
Loose Inertial Poser: Motion Capture with IMU-attached Loose-Wear Jacket | Chengxu Zuo, Yiming Wang, Lishuang Zhan, Shihui Guo, Xinyu Yi, Feng Xu, Yipeng Qin | Existing wearable motion capture methods typically demand tight on-body fixation (often using straps) for reliable sensing limiting their application in everyday life. In this paper we introduce Loose Inertial Poser a novel motion capture solution with high wearing comfortableness by integrating four Inertial Measurement Units (IMUs) into a loose-wear jacket. Specifically we address the challenge of scarce loose-wear IMU training data by proposing a Secondary Motion AutoEncoder (SeMo-AE) that learns to model and synthesize the effects of secondary motion between the skin and loose clothing on IMU data. SeMo-AE is leveraged to generate a diverse synthetic dataset of loose-wear IMU data to augment training for the pose estimation network and significantly improve its accuracy. For validation we collected a dataset with various subjects and 2 wearing styles (zipped and unzipped). Experimental results demonstrate that our approach maintains high-quality real-time posture estimation even in loose-wear scenarios. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zuo_Loose_Inertial_Poser_Motion_Capture_with_IMU-attached_Loose-Wear_Jacket_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zuo_Loose_Inertial_Poser_Motion_Capture_with_IMU-attached_Loose-Wear_Jacket_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zuo_Loose_Inertial_Poser_Motion_Capture_with_IMU-attached_Loose-Wear_Jacket_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zuo_Loose_Inertial_Poser_CVPR_2024_supplemental.mp4 | null |
Instance Tracking in 3D Scenes from Egocentric Videos | Yunhan Zhao, Haoyu Ma, Shu Kong, Charless Fowlkes | Egocentric sensors such as AR/VR devices capture human-object interactions and offer the potential to provide task-assistance by recalling 3D locations of objects of interest in the surrounding environment. This capability requires instance tracking in real-world 3D scenes from egocentric videos (IT3DEgo). We explore this problem by first introducing a new benchmark dataset consisting of RGB and depth videos per-frame camera pose and instance-level annotations in both 2D camera and 3D world coordinates. We present an evaluation protocol which evaluates tracking performance in 3D coordinates with two settings for enrolling instances to track: (1) single-view online enrollment where an instance is specified on-the-fly based on the human wearer's interactions. and (2) multi-view pre-enrollment where images of an instance to be tracked are stored in memory ahead of time. To address IT3DEgo we first re-purpose methods from relevant areas e.g. single object tracking (SOT) -- running SOT methods to track instances in 2D frames and lifting them to 3D using camera pose and depth. We also present a simple method that leverages pretrained segmentation and detection models to generate proposals from RGB frames and match proposals with enrolled instance images. Our experiments show that our method (with no finetuning) significantly outperforms SOT-based approaches in the egocentric setting. We conclude by arguing that the problem of egocentric instance tracking is made easier by leveraging camera pose and using a 3D allocentric (world) coordinate representation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_Instance_Tracking_in_3D_Scenes_from_Egocentric_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.04117 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Instance_Tracking_in_3D_Scenes_from_Egocentric_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Instance_Tracking_in_3D_Scenes_from_Egocentric_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Instance_Tracking_in_CVPR_2024_supplemental.pdf | null |
Correlation-aware Coarse-to-fine MLPs for Deformable Medical Image Registration | Mingyuan Meng, Dagan Feng, Lei Bi, Jinman Kim | Deformable image registration is a fundamental step for medical image analysis. Recently transformers have been used for registration and outperformed Convolutional Neural Networks (CNNs). Transformers can capture long-range dependence among image features which have been shown beneficial for registration. However due to the high computation/memory loads of self-attention transformers are typically used at downsampled feature resolutions and cannot capture fine-grained long-range dependence at the full image resolution. This limits deformable registration as it necessitates precise dense correspondence between each image pixel. Multi-layer Perceptrons (MLPs) without self-attention are efficient in computation/memory usage enabling the feasibility of capturing fine-grained long-range dependence at full resolution. Nevertheless MLPs have not been extensively explored for image registration and are lacking the consideration of inductive bias crucial for medical registration tasks. In this study we propose the first correlation-aware MLP-based registration network (CorrMLP) for deformable medical image registration. Our CorrMLP introduces a correlation-aware multi-window MLP block in a novel coarse-to-fine registration architecture which captures fine-grained multi-range dependence to perform correlation-aware coarse-to-fine registration. Extensive experiments with seven public medical datasets show that our CorrMLP outperforms state-of-the-art deformable registration methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Meng_Correlation-aware_Coarse-to-fine_MLPs_for_Deformable_Medical_Image_Registration_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Meng_Correlation-aware_Coarse-to-fine_MLPs_for_Deformable_Medical_Image_Registration_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Meng_Correlation-aware_Coarse-to-fine_MLPs_for_Deformable_Medical_Image_Registration_CVPR_2024_paper.html | CVPR 2024 | null | null |
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts | Jiawen Zhu, Guansong Pang | This paper explores the problem of Generalist Anomaly Detection (GAD) aiming to train one single detection model that can generalize to detect anomalies in diverse datasets from different application domains without any further training on the target data. Some recent studies have shown that large pre-trained Visual-Language Models (VLMs) like CLIP have strong generalization capabilities on detecting industrial defects from various datasets but their methods rely heavily on handcrafted text prompts about defects making them difficult to generalize to anomalies in other applications e.g. medical image anomalies or semantic anomalies in natural images. In this work we propose to train a GAD model with few-shot normal images as sample prompts for AD on diverse datasets on the fly. To this end we introduce a novel approach that learns an in-context residual learning model for GAD termed InCTRL. It is trained on an auxiliary dataset to discriminate anomalies from normal samples based on a holistic evaluation of the residuals between query images and few-shot normal sample prompts. Regardless of the datasets per definition of anomaly larger residuals are expected for anomalies than normal samples thereby enabling InCTRL to generalize across different domains without further training. Comprehensive experiments on nine AD datasets are performed to establish a GAD benchmark that encapsulate the detection of industrial defect anomalies medical anomalies and semantic anomalies in both one-vs-all and multi-class setting on which InCTRL is the best performer and significantly outperforms state-of-the-art competing methods. Code is available at https://github.com/mala-lab/InCTRL. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Toward_Generalist_Anomaly_Detection_via_In-context_Residual_Learning_with_Few-shot_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.06495 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Toward_Generalist_Anomaly_Detection_via_In-context_Residual_Learning_with_Few-shot_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Toward_Generalist_Anomaly_Detection_via_In-context_Residual_Learning_with_Few-shot_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_Toward_Generalist_Anomaly_CVPR_2024_supplemental.pdf | null |
Fourier-basis Functions to Bridge Augmentation Gap: Rethinking Frequency Augmentation in Image Classification | Puru Vaish, Shunxin Wang, Nicola Strisciuglio | Computer vision models normally witness degraded performance when deployed in real-world scenarios due to unexpected changes in inputs that were not accounted for during training. Data augmentation is commonly used to address this issue as it aims to increase data variety and reduce the distribution gap between training and test data. However common visual augmentations might not guarantee extensive robustness of computer vision models. In this paper we propose Auxiliary Fourier-basis Augmentation (AFA) a complementary technique targeting augmentation in the frequency domain and filling the robustness gap left by visual augmentations. We demonstrate the utility of augmentation via Fourier-basis additive noise in a straightforward and efficient adversarial setting. Our results show that AFA benefits the robustness of models against common corruptions OOD generalization and consistency of performance of models against increasing perturbations with negligible deficit to the standard performance of models. It can be seamlessly integrated with other augmentation techniques to further boost performance. Codes and models are available at \href https://github.com/nis-research/afa-augment https://github.com/nis-research/afa-augment . | https://openaccess.thecvf.com/content/CVPR2024/papers/Vaish_Fourier-basis_Functions_to_Bridge_Augmentation_Gap_Rethinking_Frequency_Augmentation_in_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Vaish_Fourier-basis_Functions_to_Bridge_Augmentation_Gap_Rethinking_Frequency_Augmentation_in_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Vaish_Fourier-basis_Functions_to_Bridge_Augmentation_Gap_Rethinking_Frequency_Augmentation_in_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Vaish_Fourier-basis_Functions_to_CVPR_2024_supplemental.pdf | null |
Learning to Transform Dynamically for Better Adversarial Transferability | Rongyi Zhu, Zeliang Zhang, Susan Liang, Zhuo Liu, Chenliang Xu | Adversarial examples crafted by adding perturbations imperceptible to humans can deceive neural networks. Recent studies identify the adversarial transferability across various models i.e. the cross-model attack ability of adversarial samples. To enhance such adversarial transferability existing input transformation-based methods diversify input data with transformation augmentation. However their effectiveness is limited by the finite number of available transformations. In our study we introduce a novel approach named Learning to Transform (L2T). L2T increases the diversity of transformed images by selecting the optimal combination of operations from a pool of candidates consequently improving adversarial transferability. We conceptualize the selection of optimal transformation combinations as a trajectory optimization problem and employ a reinforcement learning strategy to effectively solve the problem. Comprehensive experiments on the ImageNet dataset as well as practical tests with Google Vision and GPT-4V reveal that L2T surpasses current methodologies in enhancing adversarial transferability thereby confirming its effectiveness and practical significance. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_Learning_to_Transform_Dynamically_for_Better_Adversarial_Transferability_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.14077 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Learning_to_Transform_Dynamically_for_Better_Adversarial_Transferability_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_Learning_to_Transform_Dynamically_for_Better_Adversarial_Transferability_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhu_Learning_to_Transform_CVPR_2024_supplemental.pdf | null |
PlatoNeRF: 3D Reconstruction in Plato's Cave via Single-View Two-Bounce Lidar | Tzofi Klinghoffer, Xiaoyu Xiang, Siddharth Somasundaram, Yuchen Fan, Christian Richardt, Ramesh Raskar, Rakesh Ranjan | 3D reconstruction from a single-view is challenging because of the ambiguity from monocular cues and lack of information about occluded regions. Neural radiance fields (NeRF) while popular for view synthesis and 3D reconstruction are typically reliant on multi-view images. Existing methods for single-view 3D reconstruction with NeRF rely on either data priors to hallucinate views of occluded regions which may not be physically accurate or shadows observed by RGB cameras which are difficult to detect in ambient light and low albedo backgrounds. We propose using time-of-flight data captured by a single-photon avalanche diode to overcome these limitations. Our method models two-bounce optical paths with NeRF using lidar transient data for supervision. By leveraging the advantages of both NeRF and two-bounce light measured by lidar we demonstrate that we can reconstruct visible and occluded geometry without data priors or reliance on controlled ambient lighting or scene albedo. In addition we demonstrate improved generalization under practical constraints on sensor spatial- and temporal-resolution. We believe our method is a promising direction as single-photon lidars become ubiquitous on consumer devices such as phones tablets and headsets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Klinghoffer_PlatoNeRF_3D_Reconstruction_in_Platos_Cave_via_Single-View_Two-Bounce_Lidar_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Klinghoffer_PlatoNeRF_3D_Reconstruction_in_Platos_Cave_via_Single-View_Two-Bounce_Lidar_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Klinghoffer_PlatoNeRF_3D_Reconstruction_in_Platos_Cave_via_Single-View_Two-Bounce_Lidar_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Klinghoffer_PlatoNeRF_3D_Reconstruction_CVPR_2024_supplemental.pdf | null |
PanoContext-Former: Panoramic Total Scene Understanding with a Transformer | Yuan Dong, Chuan Fang, Liefeng Bo, Zilong Dong, Ping Tan | Panoramic images enable deeper understanding and more holistic perception of 360 surrounding environment which can naturally encode enriched scene context information compared to standard perspective image. Previous work has made lots of effort to solve the scene understanding task in a hybrid solution based on 2D-3D geometric reasoning thus each sub-task is processed separately and few correlations are explored in this procedure. In this paper we propose a fully 3D method for holistic indoor scene understanding which recovers the objects' shapes oriented bounding boxes and the 3D room layout simultaneously from a single panorama. To maximize the exploration of the rich context information we design a transformer-based context module to predict the representation and relationship among each component of the scene. In addition we introduce a new dataset for scene understanding including photo-realistic panoramas high-fidelity depth images accurately annotated room layouts oriented object bounding boxes and shapes. Experiments on the synthetic and new datasets demonstrate that our method outperforms previous panoramic scene understanding methods in terms of both layout estimation and 3D object detection. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dong_PanoContext-Former_Panoramic_Total_Scene_Understanding_with_a_Transformer_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_PanoContext-Former_Panoramic_Total_Scene_Understanding_with_a_Transformer_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dong_PanoContext-Former_Panoramic_Total_Scene_Understanding_with_a_Transformer_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dong_PanoContext-Former_Panoramic_Total_CVPR_2024_supplemental.pdf | null |
Training-Free Pretrained Model Merging | Zhengqi Xu, Ke Yuan, Huiqiong Wang, Yong Wang, Mingli Song, Jie Song | Recently model merging techniques have surfaced as a solution to combine multiple single-talent models into a single multi-talent model. However previous endeavors in this field have either necessitated additional training or fine-tuning processes or require that the models possess the same pre-trained initialization. In this work we identify a common drawback in prior works w.r.t. the inconsistency of unit similarity in the weight space and the activation space. To address this inconsistency we propose an innovative model merging framework coined as merging under dual-space constraints (MuDSC). Specifically instead of solely maximizing the objective of a single space we advocate for the exploration of permutation matrices situated in a region with a unified high similarity in the dual space achieved through the linear combination of activation and weight similarity matrices. In order to enhance usability we have also incorporated adaptations for group structure including Multi-Head Attention and Group Normalization. Comprehensive experimental comparisons demonstrate that MuDSC can significantly boost the performance of merged models with various task combinations and architectures. Furthermore the visualization of the merged model within the multi-task loss landscape reveals that MuDSC enables the merged model to reside in the overlapping segment featuring a unified lower loss for each task. Our code is publicly available at https://github.com/zju-vipa/training_free_model_merging. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Training-Free_Pretrained_Model_Merging_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01753 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Training-Free_Pretrained_Model_Merging_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Training-Free_Pretrained_Model_Merging_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Training-Free_Pretrained_Model_CVPR_2024_supplemental.zip | null |
NC-SDF: Enhancing Indoor Scene Reconstruction Using Neural SDFs with View-Dependent Normal Compensation | Ziyi Chen, Xiaolong Wu, Yu Zhang | State-of-the-art neural implicit surface representations have achieved impressive results in indoor scene reconstruction by incorporating monocular geometric priors as additional supervision. However we have observed that multi-view inconsistency between such priors poses a challenge for high-quality reconstructions. In response we present NC-SDF a neural signed distance field (SDF) 3D reconstruction framework with view-dependent normal compensation (NC). Specifically we integrate view-dependent biases in monocular normal priors into the neural implicit representation of the scene. By adaptively learning and correcting the biases our NC-SDF effectively mitigates the adverse impact of inconsistent supervision enhancing both the global consistency and local details in the reconstructions. To further refine the details we introduce an informative pixel sampling strategy to pay more attention to intricate geometry with higher information content. Additionally we design a hybrid geometry modeling approach to improve the neural implicit representation. Experiments on synthetic and real-world datasets demonstrate that NC-SDF outperforms existing approaches in terms of reconstruction quality. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_NC-SDF_Enhancing_Indoor_Scene_Reconstruction_Using_Neural_SDFs_with_View-Dependent_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_NC-SDF_Enhancing_Indoor_Scene_Reconstruction_Using_Neural_SDFs_with_View-Dependent_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_NC-SDF_Enhancing_Indoor_Scene_Reconstruction_Using_Neural_SDFs_with_View-Dependent_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_NC-SDF_Enhancing_Indoor_CVPR_2024_supplemental.pdf | null |
An Interactive Navigation Method with Effect-oriented Affordance | Xiaohan Wang, Yuehu Liu, Xinhang Song, Yuyi Liu, Sixian Zhang, Shuqiang Jiang | Visual navigation is to let the agent reach the target according to the continuous visual input. In most previous works visual navigation is usually assumed to be done in a static and ideal environment: the target is always reachable with no need to alter the environment. However the "messy" environments are more general and practical in our daily lives where the agent may get blocked by obstacles. Thus Interactive Navigation (InterNav) is introduced to navigate to the objects in more realistic "messy" environments according to the object interaction. Prior work on InterNav learns short-term interaction through extensive trials with reinforcement learning. However interaction does not guarantee efficient navigation that is planning obstacle interactions that make shorter paths and consume less effort is also crucial. In this paper we introduce an effect-oriented affordance map to enable long-term interactive navigation extending the existing map-based navigation framework to the domain of dynamic environment. We train a set of affordance functions predicting available interactions and the time cost of removing obstacles which informatively support an interactive modular system to address interaction and long-term planning. Experiments on the ProcTHOR simulator demonstrate the capability of our affordance-driven system in long-term navigation in complex dynamic environments. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_An_Interactive_Navigation_Method_with_Effect-oriented_Affordance_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_An_Interactive_Navigation_Method_with_Effect-oriented_Affordance_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_An_Interactive_Navigation_Method_with_Effect-oriented_Affordance_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_An_Interactive_Navigation_CVPR_2024_supplemental.pdf | null |
Person in Place: Generating Associative Skeleton-Guidance Maps for Human-Object Interaction Image Editing | ChangHee Yang, ChanHee Kang, Kyeongbo Kong, Hanni Oh, Suk-Ju Kang | Recently there were remarkable advances in image editing tasks in various ways. Nevertheless existing image editing models are not designed for Human-Object Interaction (HOI) image editing. One of these approaches (e.g. ControlNet) employs the skeleton guidance to offer precise representations of human showing better results in HOI image editing. However using conventional methods manually creating HOI skeleton guidance is necessary. This paper proposes the object interactive diffuser with associative attention that considers both the interaction with objects and the joint graph structure automating the generation of HOI skeleton guidance. Additionally we propose the HOI loss with novel scaling parameter demonstrating its effectiveness in generating skeletons that interact better. To evaluate generated object-interactive skeletons we propose two metrics top-N accuracy and skeleton probabilistic distance. Our framework integrates object interactive diffuser that generates object-interactive skeletons with previous methods demonstrating the outstanding results in HOI image editing. Finally we present potentials of our framework beyond HOI image editing as applications to human-to-human interaction skeleton editing and 3D mesh optimization. The code is available at https://github.com/YangChangHee/CVPR2024_Person-In-Place_RELEASE | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Person_in_Place_Generating_Associative_Skeleton-Guidance_Maps_for_Human-Object_Interaction_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Person_in_Place_Generating_Associative_Skeleton-Guidance_Maps_for_Human-Object_Interaction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Person_in_Place_Generating_Associative_Skeleton-Guidance_Maps_for_Human-Object_Interaction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_Person_in_Place_CVPR_2024_supplemental.pdf | null |
PREGO: Online Mistake Detection in PRocedural EGOcentric Videos | Alessandro Flaborea, Guido Maria D'Amely di Melendugno, Leonardo Plini, Luca Scofano, Edoardo De Matteis, Antonino Furnari, Giovanni Maria Farinella, Fabio Galasso | Promptly identifying procedural errors from egocentric videos in an online setting is highly challenging and valuable for detecting mistakes as soon as they happen. This capability has a wide range of applications across various fields such as manufacturing and healthcare. The nature of procedural mistakes is open-set since novel types of failures might occur which calls for one-class classifiers trained on correctly executed procedures. However no technique can currently detect open-set procedural mistakes online. We propose PREGO the first online one-class classification model for mistake detection in PRocedural EGOcentric videos. PREGO is based on an online action recognition component to model the current action and a symbolic reasoning module to predict the next actions. Mistake detection is performed by comparing the recognized current action with the expected future one. We evaluate PREGO on two procedural egocentric video datasets Assembly101 and Epic-tent which we adapt for online benchmarking of procedural mistake detection to establish suitable benchmarks thus defining the Assembly101-O and Epic-tent-O datasets respectively. The code is available at https://github.com/aleflabo/PREGO | https://openaccess.thecvf.com/content/CVPR2024/papers/Flaborea_PREGO_Online_Mistake_Detection_in_PRocedural_EGOcentric_Videos_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01933 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Flaborea_PREGO_Online_Mistake_Detection_in_PRocedural_EGOcentric_Videos_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Flaborea_PREGO_Online_Mistake_Detection_in_PRocedural_EGOcentric_Videos_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Flaborea_PREGO_Online_Mistake_CVPR_2024_supplemental.pdf | null |
ChatPose: Chatting about 3D Human Pose | Yao Feng, Jing Lin, Sai Kumar Dwivedi, Yu Sun, Priyanka Patel, Michael J. Black | We introduce ChatPose a framework employing Large Language Models (LLMs) to understand and reason about 3D human poses from images or textual descriptions. Our work is motivated by the human ability to intuitively understand postures from a single image or a brief description a process that intertwines image interpretation world knowledge and an understanding of body language. Traditional human pose estimation and generation methods often operate in isolation lacking semantic understanding and reasoning abilities. ChatPose addresses these limitations by embedding SMPL poses as distinct signal tokens within a multimodal LLM enabling the direct generation of 3D body poses from both textual and visual inputs. Leveraging the powerful capabilities of multimodal LLMs ChatPose unifies classical 3D human pose and generation tasks while offering user interactions. Additionally ChatPose empowers LLMs to apply their extensive world knowledge in reasoning about human poses leading to two advanced tasks: speculative pose generation and reasoning about pose estimation. These tasks involve reasoning about humans to generate 3D poses from subtle text queries possibly accompanied by images. We establish benchmarks for these tasks moving beyond traditional 3D pose generation and estimation methods. Our results show that ChatPose out-performs existing multimodal LLMs and task-specific methods on these newly proposed tasks. Furthermore ChatPose's ability to understand and generate 3D human poses based on complex reasoning opens new directions in human pose analysis. Code and data are available for research at https://yfeng95.github.io/ChatPose. | https://openaccess.thecvf.com/content/CVPR2024/papers/Feng_ChatPose_Chatting_about_3D_Human_Pose_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18836 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Feng_ChatPose_Chatting_about_3D_Human_Pose_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Feng_ChatPose_Chatting_about_3D_Human_Pose_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Feng_ChatPose_Chatting_about_CVPR_2024_supplemental.pdf | null |
Prompt3D: Random Prompt Assisted Weakly-Supervised 3D Object Detection | Xiaohong Zhang, Huisheng Ye, Jingwen Li, Qinyu Tang, Yuanqi Li, Yanwen Guo, Jie Guo | The prohibitive cost of annotations for fully supervised 3D indoor object detection limits its practicality. In this work we propose Random Prompt Assisted Weakly-supervised 3D Object Detection termed as Prompt3D a weakly-supervised approach that leverages position-level labels to overcome this challenge. Explicitly our method focuses on enhancing labeling using synthetic scenes crafted from 3D shapes generated via random prompts. First a Synthetic Scene Generation (SSG) module is introduced to assemble synthetic scenes with a curated collection of 3D shapes created via random prompts for each category. These scenes are enriched with automatically generated point-level annotations providing a robust supervisory framework for training the detection algorithm. To enhance the transfer of knowledge from virtual to real datasets we then introduce a Prototypical Proposal Feature Alignment (PPFA) module. This module effectively alleviates the domain gap by directly minimizing the distance between feature prototypes of the same class proposals across two domains. Compared with sota BR our method improves by 5.4% and 8.7% on mAP with VoteNet and GroupFree3D serving as detectors respectively demonstrating the effectiveness of our proposed method. Code is available at: https://github.com/huishengye/prompt3d. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Prompt3D_Random_Prompt_Assisted_Weakly-Supervised_3D_Object_Detection_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Prompt3D_Random_Prompt_Assisted_Weakly-Supervised_3D_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Prompt3D_Random_Prompt_Assisted_Weakly-Supervised_3D_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Prompt3D_Random_Prompt_CVPR_2024_supplemental.pdf | null |
Logit Standardization in Knowledge Distillation | Shangquan Sun, Wenqi Ren, Jingzhi Li, Rui Wang, Xiaochun Cao | Knowledge distillation involves transferring soft labels from a teacher to a student using a shared temperature-based softmax function. However the assumption of a shared temperature between teacher and student implies a mandatory exact match between their logits in terms of logit range and variance. This side-effect limits the performance of student considering the capacity discrepancy between them and the finding that the innate logit relations of teacher are sufficient for student to learn. To address this issue we propose setting the temperature as the weighted standard deviation of logit and performing a plug-and-play Z-score pre-process of logit standardization before applying softmax and Kullback-Leibler divergence. Our pre-process enables student to focus on essential logit relations from teacher rather than requiring a magnitude match and can improve the performance of existing logit-based distillation methods. We also show a typical case where the conventional setting of sharing temperature between teacher and student cannot reliably yield the authentic distillation evaluation; nonetheless this challenge is successfully alleviated by our Z-score. We extensively evaluate our method for various student and teacher models on CIFAR-100 and ImageNet showing its significant superiority. The vanilla knowledge distillation powered by our pre-process can achieve favorable performance against state-of-the-art methods and other distillation variants can obtain considerable gain with the assistance of our pre-process. The codes pre-trained models and logs are released on Github. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_Logit_Standardization_in_Knowledge_Distillation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01427 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Logit_Standardization_in_Knowledge_Distillation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_Logit_Standardization_in_Knowledge_Distillation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_Logit_Standardization_in_CVPR_2024_supplemental.pdf | null |
Fine-grained Prototypical Voting with Heterogeneous Mixup for Semi-supervised 2D-3D Cross-modal Retrieval | Fan Zhang, Xian-Sheng Hua, Chong Chen, Xiao Luo | This paper studies the problem of semi-supervised 2D-3D retrieval which aims to align both labeled and unlabeled 2D and 3D data into the same embedding space. The problem is challenging due to the complicated heterogeneous relationships between 2D and 3D data. Moreover label scarcity in real-world applications hinders from generating discriminative representations. In this paper we propose a semi-supervised approach named Fine-grained Prototypcical Voting with Heterogeneous Mixup (FIVE) which maps both 2D and 3D data into a common embedding space for cross-modal retrieval. Specifically we generate fine-grained prototypes to model inter-class variation for both 2D and 3D data. Then considering each unlabeled sample as a query we retrieve relevant prototypes to vote for reliable and robust pseudo-labels which serve as guidance for discriminative learning under label scarcity. Furthermore to bridge the semantic gap between two modalities we mix cross-modal pairs with similar semantics in the embedding space and then perform similarity learning for cross-modal discrepancy reduction in a soft manner. The whole FIVE is optimized with the consideration of sharpness to mitigate the impact of potential label noise. Extensive experiments on benchmark datasets validate the superiority of FIVE compared with a range of baselines in different settings. On average FIVE outperforms the second-best approach by 4.74% on 3D MNIST 12.94% on ModelNet10 and 22.10% on ModelNet40. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Fine-grained_Prototypical_Voting_with_Heterogeneous_Mixup_for_Semi-supervised_2D-3D_Cross-modal_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Fine-grained_Prototypical_Voting_with_Heterogeneous_Mixup_for_Semi-supervised_2D-3D_Cross-modal_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Fine-grained_Prototypical_Voting_with_Heterogeneous_Mixup_for_Semi-supervised_2D-3D_Cross-modal_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Fine-grained_Prototypical_Voting_CVPR_2024_supplemental.pdf | null |
Leak and Learn: An Attacker's Cookbook to Train Using Leaked Data from Federated Learning | Joshua C. Zhao, Ahaan Dabholkar, Atul Sharma, Saurabh Bagchi | Federated learning is a decentralized learning paradigm introduced to preserve privacy of client data. Despite this prior work has shown that an attacker at the server can still reconstruct the private training data using only the client updates. These attacks are known as data reconstruction attacks and fall into two major categories: gradient inversion (GI) and linear layer leakage attacks (LLL). However despite demonstrating the effectiveness of these attacks in breaching privacy prior work has not investigated the usefulness of the reconstructed data for downstream tasks. In this work we explore data reconstruction attacks through the lens of training and improving models with leaked data. We demonstrate the effectiveness of both GI and LLL attacks in maliciously training models using the leaked data more accurately than a benign federated learning strategy. Counter-intuitively this bump in training quality can occur despite limited reconstruction quality or a small total number of leaked images. Finally we show the limitations of these attacks for downstream training individually for GI attacks and for LLL attacks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_Leak_and_Learn_An_Attackers_Cookbook_to_Train_Using_Leaked_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Leak_and_Learn_An_Attackers_Cookbook_to_Train_Using_Leaked_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Leak_and_Learn_An_Attackers_Cookbook_to_Train_Using_Leaked_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Leak_and_Learn_CVPR_2024_supplemental.pdf | null |
OCAI: Improving Optical Flow Estimation by Occlusion and Consistency Aware Interpolation | Jisoo Jeong, Hong Cai, Risheek Garrepalli, Jamie Menjay Lin, Munawar Hayat, Fatih Porikli | The scarcity of ground-truth labels poses one major challenge in developing optical flow estimation models that are both generalizable and robust. While current methods rely on data augmentation they have yet to fully exploit the rich information available in labeled video sequences. We propose OCAI a method that supports robust frame interpolation by generating intermediate video frames alongside optical flows in between. Utilizing a forward warping approach OCAI employs occlusion awareness to resolve ambiguities in pixel values and fills in missing values by leveraging the forward-backward consistency of optical flows. Additionally we introduce a teacher-student style semi-supervised learning method on top of the interpolated frames. Using a pair of unlabeled frames and the teacher model's predicted optical flow we generate interpolated frames and flows to train a student model. The teacher's weights are maintained using Exponential Moving Averaging of the student. Our evaluations demonstrate perceptually superior interpolation quality and enhanced optical flow accuracy on established benchmarks such as Sintel and KITTI. | https://openaccess.thecvf.com/content/CVPR2024/papers/Jeong_OCAI_Improving_Optical_Flow_Estimation_by_Occlusion_and_Consistency_Aware_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18092 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Jeong_OCAI_Improving_Optical_Flow_Estimation_by_Occlusion_and_Consistency_Aware_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Jeong_OCAI_Improving_Optical_Flow_Estimation_by_Occlusion_and_Consistency_Aware_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Jeong_OCAI_Improving_Optical_CVPR_2024_supplemental.pdf | null |
Distilling ODE Solvers of Diffusion Models into Smaller Steps | Sanghwan Kim, Hao Tang, Fisher Yu | Abstract Diffusion models have recently gained prominence as a novel category of generative models. Despite their success these models face a notable drawback in terms of slow sampling speeds requiring a high number of function evaluations (NFE) in the order of hundreds or thousands. In response both learning-free and learning-based sampling strategies have been explored to expedite the sampling process. Learning-free sampling employs various ordinary differential equation (ODE) solvers based on the formulation of diffusion ODEs. However it encounters challenges in faithfully tracking the true sampling trajectory particularly for small NFE. Conversely learning-based sampling methods such as knowledge distillation demand extensive additional training limiting their practical applicability. To overcome these limitations we introduce Distilled-ODE solvers (D-ODE solvers) a straightforward distillation approach grounded in ODE solver formulations. Our method seamlessly integrates the strengths of both learning-free and learning-based sampling. D-ODE solvers are constructed by introducing a single parameter adjustment to existing ODE solvers. Furthermore we optimize D-ODE solvers with smaller steps using knowledge distillation from ODE solvers with larger steps across a batch of samples. Comprehensive experiments demonstrate the superior performance of D-ODE solvers compared to existing ODE solvers including DDIM PNDM DPM-Solver DEIS and EDM particularly in scenarios with fewer NFE. Notably our method incurs negligible computational overhead compared to previous distillation techniques facilitating straightforward and rapid integration with existing samplers. Qualitative analysis reveals that D-ODE solvers not only enhance image quality but also faithfully follow the target ODE trajectory. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kim_Distilling_ODE_Solvers_of_Diffusion_Models_into_Smaller_Steps_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.16421 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Distilling_ODE_Solvers_of_Diffusion_Models_into_Smaller_Steps_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kim_Distilling_ODE_Solvers_of_Diffusion_Models_into_Smaller_Steps_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kim_Distilling_ODE_Solvers_CVPR_2024_supplemental.pdf | null |
Navigating Beyond Dropout: An Intriguing Solution towards Generalizable Image Super Resolution | Hongjun Wang, Jiyuan Chen, Yinqiang Zheng, Tieyong Zeng | Deep learning has led to a dramatic leap on Single Image Super-Resolution (SISR) performances in recent years. While most existing work assumes a simple and fixed degradation model (e.g. bicubic downsampling) the research of Blind SR seeks to improve model generalization ability with unknown degradation. Recently Kong et al. pioneer the investigation of a more suitable training strategy for Blind SR using Dropout. Although such method indeed brings substantial generalization improvements via mitigating overfitting we argue that Dropout simultaneously introduces undesirable side-effect that compromises model's capacity to faithfully reconstruct fine details. We show both the theoretical and experimental analyses in our paper and furthermore we present another easy yet effective training strategy that enhances the generalization ability of the model by simply modulating its first and second-order features statistics. Experimental results have shown that our method could serve as a model-agnostic regularization and outperforms Dropout on seven benchmark datasets including both synthetic and real-world scenarios. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Navigating_Beyond_Dropout_An_Intriguing_Solution_towards_Generalizable_Image_Super_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18929 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Navigating_Beyond_Dropout_An_Intriguing_Solution_towards_Generalizable_Image_Super_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Navigating_Beyond_Dropout_An_Intriguing_Solution_towards_Generalizable_Image_Super_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Navigating_Beyond_Dropout_CVPR_2024_supplemental.pdf | null |
Doodle Your 3D: From Abstract Freehand Sketches to Precise 3D Shapes | Hmrishav Bandyopadhyay, Subhadeep Koley, Ayan Das, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song | In this paper we democratise 3D content creation enabling precise generation of 3D shapes from abstract sketches while overcoming limitations tied to drawing skills. We introduce a novel part-level modelling and alignment framework that facilitates abstraction modelling and cross-modal correspondence. Leveraging the same part-level decoder our approach seamlessly extends to sketch modelling by establishing correspondence between CLIPasso edgemaps and projected 3D part regions eliminating the need for a dataset pairing human sketches and 3D shapes. Additionally our method introduces a seamless in-position editing process as a byproduct of cross-modal part-aligned modelling. Operating in a low-dimensional implicit space our approach significantly reduces computational demands and processing time. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bandyopadhyay_Doodle_Your_3D_From_Abstract_Freehand_Sketches_to_Precise_3D_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.04043 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bandyopadhyay_Doodle_Your_3D_From_Abstract_Freehand_Sketches_to_Precise_3D_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bandyopadhyay_Doodle_Your_3D_From_Abstract_Freehand_Sketches_to_Precise_3D_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Bandyopadhyay_Doodle_Your_3D_CVPR_2024_supplemental.pdf | null |
LightIt: Illumination Modeling and Control for Diffusion Models | Peter Kocsis, Julien Philip, Kalyan Sunkavalli, Matthias Nießner, Yannick Hold-Geoffroy | We introduce LightIt a method for explicit illumination control for image generation. Recent generative methods lack lighting control which is crucial to numerous artistic aspects of image generation such as setting the overall mood or cinematic appearance. To overcome these limitations we propose to condition the generation on shading and normal maps. We model the lighting with single bounce shading which includes cast shadows. We first train a shading estimation module to generate a dataset of real-world images and shading pairs. Then we train a control network using the estimated shading and normals as input. Our method demonstrates high-quality image generation and lighting control in numerous scenes. Additionally we use our generated dataset to train an identity-preserving relighting model conditioned on an image and a target shading. Our method is the first that enables the generation of images with controllable consistent lighting and performs on par with specialized relighting state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Kocsis_LightIt_Illumination_Modeling_and_Control_for_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10615 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Kocsis_LightIt_Illumination_Modeling_and_Control_for_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Kocsis_LightIt_Illumination_Modeling_and_Control_for_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Kocsis_LightIt_Illumination_Modeling_CVPR_2024_supplemental.pdf | null |
Single View Refractive Index Tomography with Neural Fields | Brandon Zhao, Aviad Levis, Liam Connor, Pratul P. Srinivasan, Katherine L. Bouman | Refractive Index Tomography is the inverse problem of reconstructing the continuously-varying 3D refractive index in a scene using 2D projected image measurements. Although a purely refractive field is not directly visible it bends light rays as they travel through space thus providing a signal for reconstruction. The effects of such fields appear in many scientific computer vision settings ranging from refraction due to transparent cells in microscopy to the lensing of distant galaxies caused by dark matter in astrophysics. Reconstructing these fields is particularly difficult due to the complex nonlinear effects of the refractive field on observed images. Furthermore while standard 3D reconstruction and tomography settings typically have access to observations of the scene from many viewpoints many refractive index tomography problem settings only have access to images observed from a single viewpoint. We introduce a method that leverages prior knowledge of light sources scattered throughout the refractive medium to help disambiguate the single-view refractive index tomography problem. We differentiably trace curved rays through a neural field representation of the refractive field and optimize its parameters to best reproduce the observed image. We demonstrate the efficacy of our approach by reconstructing simulated refractive fields analyze the effects of light source distribution on the recovered field and test our method on a simulated dark matter mapping problem where we successfully recover the 3D refractive field caused by a realistic dark matter distribution. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_Single_View_Refractive_Index_Tomography_with_Neural_Fields_CVPR_2024_paper.pdf | http://arxiv.org/abs/2309.04437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Single_View_Refractive_Index_Tomography_with_Neural_Fields_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_Single_View_Refractive_Index_Tomography_with_Neural_Fields_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_Single_View_Refractive_CVPR_2024_supplemental.zip | null |
Neural Lineage | Runpeng Yu, Xinchao Wang | Given a well-behaved neural network is possible to identify its parent based on which it was tuned? In this paper we introduce a novel task known as neural lineage detection aiming at discovering lineage relationships between parent and child models. Specifically from a set of parent models neural lineage detection predicts which parent model a child model has been fine-tuned from. We propose two approaches to address this task. (1) For practical convenience we introduce a learning-free approach which integrates an approximation of the finetuning process into the neural network representation similarity metrics leading to a similarity-based lineage detection scheme. (2) For the pursuit of accuracy we introduce a learning-based lineage detector comprising encoders and a transformer detector. Through experimentation we have validated that our proposed learning-free and learning-based methods outperform the baseline in various learning settings and are adaptable to a variety of visual models. Moreover they also exhibit the ability to trace cross-generational lineage identifying not only parent models but also their ancestors. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Neural_Lineage_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Neural_Lineage_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Neural_Lineage_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_Neural_Lineage_CVPR_2024_supplemental.pdf | null |
Visual Layout Composer: Image-Vector Dual Diffusion Model for Design Layout Generation | Mohammad Amin Shabani, Zhaowen Wang, Difan Liu, Nanxuan Zhao, Jimei Yang, Yasutaka Furukawa | This paper proposes an image-vector dual diffusion model for generative layout design. Distinct from prior efforts that mostly ignore element-level visual information our approach integrates the power of a pre-trained large image diffusion model to guide layout composition in a vector diffusion model by providing enhanced salient region understanding and high-level inter-element relationship reasoning. Our proposed model simultaneously operates in two domains: it generates the overall design appearance in the image domain while optimizing the size and position of each design element in the vector domain. The proposed method achieves the state-of-the-art results on several datasets and enables new layout design applications. | https://openaccess.thecvf.com/content/CVPR2024/papers/Shabani_Visual_Layout_Composer_Image-Vector_Dual_Diffusion_Model_for_Design_Layout_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Shabani_Visual_Layout_Composer_Image-Vector_Dual_Diffusion_Model_for_Design_Layout_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Shabani_Visual_Layout_Composer_Image-Vector_Dual_Diffusion_Model_for_Design_Layout_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Shabani_Visual_Layout_Composer_CVPR_2024_supplemental.pdf | null |
FC-GNN: Recovering Reliable and Accurate Correspondences from Interferences | Haobo Xu, Jun Zhou, Hua Yang, Renjie Pan, Cunyan Li | Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades. However matching noise within and between images along with inconsistent keypoint detection frequently degrades the matching performance. We review these problems and thus propose: 1) a novel and unified Filtering and Calibrating (FC) approach that jointly rejects outliers and optimizes inliers and 2) leveraging both the matching context and the underlying image texture to remove matching uncertainties. Under the guidance of the above innovations we construct Filtering and Calibrating Graph Neural Network (FC-GNN) which follows the FC approach to recover reliable and accurate correspondences from various interferences. FC-GNN conducts an effectively combined inference of contextual and local information through careful embedding and multiple information aggregations predicting confidence scores and calibration offsets for the input correspondences to jointly filter out outliers and improve pixel-level matching accuracy. Moreover we exploit the local coherence of matches to perform inference on local graphs thereby reducing computational complexity. Overall FC-GNN operates at lightning speed and can greatly boost the performance of diverse matching pipelines across various tasks showcasing the immense potential of such approaches to become standard and pivotal components of image matching. Code is avaiable at https://github.com/xuy123456/fcgnn. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_FC-GNN_Recovering_Reliable_and_Accurate_Correspondences_from_Interferences_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_FC-GNN_Recovering_Reliable_and_Accurate_Correspondences_from_Interferences_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_FC-GNN_Recovering_Reliable_and_Accurate_Correspondences_from_Interferences_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_FC-GNN_Recovering_Reliable_CVPR_2024_supplemental.pdf | null |
Turb-Seg-Res: A Segment-then-Restore Pipeline for Dynamic Videos with Atmospheric Turbulence | Ripon Kumar Saha, Dehao Qin, Nianyi Li, Jinwei Ye, Suren Jayasuriya | Tackling image degradation due to atmospheric turbulence particularly in dynamic environments remains a challenge for long-range imaging systems. Existing techniques have been primarily designed for static scenes or scenes with small motion. This paper presents the first segment-then-restore pipeline for restoring the videos of dynamic scenes in turbulent environments. We leverage mean optical flow with an unsupervised motion segmentation method to separate dynamic and static scene components prior to restoration. After camera shake compensation and segmentation we introduce foreground/background enhancement leveraging the statistics of turbulence strength and a transformer model trained on a novel noise-based procedural turbulence generator for fast dataset augmentation. Benchmarked against existing restoration methods our approach restores most of the geometric distortion and enhances the sharpness of videos. We make our code simulator and data publicly available to advance the field of video restoration from turbulence: riponcs.github.io/TurbSegRes | https://openaccess.thecvf.com/content/CVPR2024/papers/Saha_Turb-Seg-Res_A_Segment-then-Restore_Pipeline_for_Dynamic_Videos_with_Atmospheric_Turbulence_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Saha_Turb-Seg-Res_A_Segment-then-Restore_Pipeline_for_Dynamic_Videos_with_Atmospheric_Turbulence_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Saha_Turb-Seg-Res_A_Segment-then-Restore_Pipeline_for_Dynamic_Videos_with_Atmospheric_Turbulence_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Saha_Turb-Seg-Res_A_Segment-then-Restore_CVPR_2024_supplemental.pdf | null |
Real-time Acquisition and Reconstruction of Dynamic Volumes with Neural Structured Illumination | Yixin Zeng, Zoubin Bi, Mingrui Yin, Xiang Feng, Kun Zhou, Hongzhi Wu | We propose a novel framework for real-time acquisition and reconstruction of temporally-varying 3D phenomena with high quality. The core of our framework is a deep neural network with an encoder that directly maps to the structured illumination during acquisition a decoder that predicts a 1D density distribution from single-pixel measurements under the optimized lighting and an aggregation module that combines the predicted densities for each camera into a single volume. It enables the automatic and joint optimization of physical acquisition and computational reconstruction and is flexible to adapt to different hardware configurations. The effectiveness of our framework is demonstrated on a lightweight setup with an off-the-shelf projector and one or multiple cameras achieving a performance of 40 volumes per second at a spatial resolution of 128^3. We compare favorably with state-of-the-art techniques in real and synthetic experiments and evaluate the impact of various factors over our pipeline. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zeng_Real-time_Acquisition_and_Reconstruction_of_Dynamic_Volumes_with_Neural_Structured_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_Real-time_Acquisition_and_Reconstruction_of_Dynamic_Volumes_with_Neural_Structured_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zeng_Real-time_Acquisition_and_Reconstruction_of_Dynamic_Volumes_with_Neural_Structured_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zeng_Real-time_Acquisition_and_CVPR_2024_supplemental.zip | null |
3D Multi-frame Fusion for Video Stabilization | Zhan Peng, Xinyi Ye, Weiyue Zhao, Tianqi Liu, Huiqiang Sun, Baopu Li, Zhiguo Cao | In this paper we present RStab a novel framework for video stabilization that integrates 3D multi-frame fusion through volume rendering. Departing from conventional methods we introduce a 3D multi-frame perspective to generate stabilized images addressing the challenge of full-frame generation while preserving structure. The core of our RStab framework lies in Stabilized Rendering (SR) a volume rendering module fusing multi-frame information in 3D space. Specifically SR involves warping features and colors from multiple frames by projection fusing them into descriptors to render the stabilized image. However the precision of warped information depends on the projection accuracy a factor significantly influenced by dynamic regions. In response we introduce the Adaptive Ray Range (ARR) module to integrate depth priors adaptively defining the sampling range for the projection process. Additionally we propose Color Correction (CC) assisting geometric constraints with optical flow for accurate color aggregation. Thanks to the three modules our RStab demonstrates superior performance compared with previous stabilizers in the field of view (FOV) image quality and video stability across various datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Peng_3D_Multi-frame_Fusion_for_Video_Stabilization_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.12887 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_3D_Multi-frame_Fusion_for_Video_Stabilization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_3D_Multi-frame_Fusion_for_Video_Stabilization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Peng_3D_Multi-frame_Fusion_CVPR_2024_supplemental.pdf | null |
Local-consistent Transformation Learning for Rotation-invariant Point Cloud Analysis | Yiyang Chen, Lunhao Duan, Shanshan Zhao, Changxing Ding, Dacheng Tao | Rotation invariance is an important requirement for point shape analysis. To achieve this current state-of-the-art methods attempt to construct the local rotation-invariant representation through learning or defining the local reference frame (LRF). Although efficient these LRF-based methods suffer from perturbation of local geometric relations resulting in suboptimal local rotation invariance. To alleviate this issue we propose a Local-consistent Transformation (LocoTrans) learning strategy. Specifically we first construct the local-consistent reference frame (LCRF) by considering the symmetry of the two axes in LRF. In comparison with previous LRFs our LCRF is able to preserve local geometric relationships better through performing local-consistent transformation. However as the consistency only exists in local regions the relative pose information is still lost in the intermediate layers of the network. We mitigate such a relative pose issue by developing a relative pose recovery (RPR) module. RPR aims to restore the relative pose between adjacent transformed patches. Equipped with LCRF and RPR our LocoTrans is capable of learning local-consistent transformation and preserving local geometry which benefits rotation invariance learning. Competitive performance under arbitrary rotations on both shape classification and part segmentation tasks and ablations can demonstrate the effectiveness of our method. Code will be available publicly at https://github.com/wdttt/LocoTrans. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Local-consistent_Transformation_Learning_for_Rotation-invariant_Point_Cloud_Analysis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.11113 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Local-consistent_Transformation_Learning_for_Rotation-invariant_Point_Cloud_Analysis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Local-consistent_Transformation_Learning_for_Rotation-invariant_Point_Cloud_Analysis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Local-consistent_Transformation_Learning_CVPR_2024_supplemental.pdf | null |
Tailored Visions: Enhancing Text-to-Image Generation with Personalized Prompt Rewriting | Zijie Chen, Lichao Zhang, Fangsheng Weng, Lili Pan, Zhenzhong Lan | Despite significant progress in the field it is still challenging to create personalized visual representations that align closely with the desires and preferences of individual users. This process requires users to articulate their ideas in words that are both comprehensible to the models and accurately capture their vision posing difficulties for many users. In this paper we tackle this challenge by leveraging historical user interactions with the system to enhance user prompts. We propose a novel approach that involves rewriting user prompts based on a newly collected large-scale text-to-image dataset with over 300k prompts from 3115 users. Our rewriting model enhances the expressiveness and alignment of user prompts with their intended visual outputs. Experimental results demonstrate the superiority of our methods over baseline approaches as evidenced in our new offline evaluation method and online tests. Our code and dataset are available at https://github.com/zzjchen/Tailored-Visions | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Tailored_Visions_Enhancing_Text-to-Image_Generation_with_Personalized_Prompt_Rewriting_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.08129 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Tailored_Visions_Enhancing_Text-to-Image_Generation_with_Personalized_Prompt_Rewriting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Tailored_Visions_Enhancing_Text-to-Image_Generation_with_Personalized_Prompt_Rewriting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Tailored_Visions_Enhancing_CVPR_2024_supplemental.pdf | null |
Efficient Deformable ConvNets: Rethinking Dynamic and Sparse Operator for Vision Applications | Yuwen Xiong, Zhiqi Li, Yuntao Chen, Feng Wang, Xizhou Zhu, Jiapeng Luo, Wenhai Wang, Tong Lu, Hongsheng Li, Yu Qiao, Lewei Lu, Jie Zhou, Jifeng Dai | We introduce Deformable Convolution v4 (DCNv4) a highly efficient and effective operator designed for a broad spectrum of vision applications. DCNv4 addresses the limitations of its predecessor DCNv3 with two key enhancements: 1. removing softmax normalization in spatial aggregation to enhance its dynamic property and expressive power and 2. optimizing memory access to minimize redundant operations for speedup. These improvements result in a significantly faster convergence compared to DCNv3 and a substantial increase in processing speed with DCNv4 achieving more than three times the forward speed. DCNv4 demonstrates exceptional performance across various tasks including image classification instance and semantic segmentation and notably image generation. When integrated into generative models like U-Net in the latent diffusion model DCNv4 outperforms its baseline underscoring its possibility to enhance generative models. In practical applications replacing DCNv3 with DCNv4 in the InternImage model to create FlashInternImage results in up to 80% speed increase and further performance improvement without further modifications. The advancements in speed and efficiency of DCNv4 combined with its robust performance across diverse vision tasks show its potential as a foundational building block for future vision models. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xiong_Efficient_Deformable_ConvNets_Rethinking_Dynamic_and_Sparse_Operator_for_Vision_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.06197 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xiong_Efficient_Deformable_ConvNets_Rethinking_Dynamic_and_Sparse_Operator_for_Vision_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xiong_Efficient_Deformable_ConvNets_Rethinking_Dynamic_and_Sparse_Operator_for_Vision_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xiong_Efficient_Deformable_ConvNets_CVPR_2024_supplemental.pdf | null |
CoDe: An Explicit Content Decoupling Framework for Image Restoration | Enxuan Gu, Hongwei Ge, Yong Guo | The performance of image restoration (IR) is highly dependent on the reconstruction quality of diverse contents with varying complexity. However most IR approaches model the mapping between various complexity contents of inputs and outputs through the repeated feature calculation propagation mechanism in a unified pipeline which leads to unsatisfactory results. To address this issue we propose an explicit Content Decoupling framework for IR dubbed CoDe to end-to-end model the restoration process by utilizing decoupled content components in a divide-and-conquer-like architecture. Specifically a Content Decoupling Module is first designed to decouple content components of inputs and outputs according to the frequency spectra adaptively generated from the transform domain. In addition in order to harness the divide-and-conquer strategy for reconstructing decoupled content components we propose an IR Network Container. It contains an optimized version which is a streamlining of an arbitrary IR network comprising the cascaded modulated subnets and a Reconstruction Layers Pool. Finally a Content Consistency Loss is designed from the transform domain perspective to supervise the restoration process of each content component and further guide the feature fusion process. Extensive experiments on several IR tasks such as image super-resolution image denoising and image blurring covering both real and synthetic settings demonstrate that the proposed paradigm can effectively take the performance of the original network to a new state-of-the-art level in multiple benchmark datasets (e.g. 0.34dB@Set5 x4 over DAT). | https://openaccess.thecvf.com/content/CVPR2024/papers/Gu_CoDe_An_Explicit_Content_Decoupling_Framework_for_Image_Restoration_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Gu_CoDe_An_Explicit_Content_Decoupling_Framework_for_Image_Restoration_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Gu_CoDe_An_Explicit_Content_Decoupling_Framework_for_Image_Restoration_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Gu_CoDe_An_Explicit_CVPR_2024_supplemental.pdf | null |
XFibrosis: Explicit Vessel-Fiber Modeling for Fibrosis Staging from Liver Pathology Images | Chong Yin, Siqi Liu, Fei Lyu, Jiahao Lu, Sune Darkner, Vincent Wai-Sun Wong, Pong C. Yuen | The increasing prevalence of non-alcoholic fatty liver disease (NAFLD) has caused public concern in recent years. The high prevalence and risk of severe complications make monitoring NAFLD progression a public health priority. Fibrosis staging from liver biopsy images plays a key role in demonstrating the histological progression of NAFLD. Fibrosis mainly involves the deposition of fibers around vessels. Current deep learning-based fibrosis staging methods learn spatial relationships between tissue patches but do not explicitly consider the relationships between vessels and fibers leading to limited performance and poor interpretability. In this paper we propose an eXplicit vessel-fiber modeling method for Fibrosis staging from liver biopsy images namely XFibrosis. Specifically we transform vessels and fibers into graph-structured representations where their micro-structures are depicted by vessel-induced primal graphs and fiber-induced dual graphs respectively. Moreover the fiber-induced dual graphs also represent the connectivity information between vessels caused by fiber deposition. A primal-dual graph convolution module is designed to facilitate the learning of spatial relationships between vessels and fibers allowing for the joint exploration and interaction of their micro-structures. Experiments conducted on two datasets have shown that explicitly modeling the relationship between vessels and fibers leads to improved fibrosis staging and enhanced interpretability. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yin_XFibrosis_Explicit_Vessel-Fiber_Modeling_for_Fibrosis_Staging_from_Liver_Pathology_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yin_XFibrosis_Explicit_Vessel-Fiber_Modeling_for_Fibrosis_Staging_from_Liver_Pathology_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yin_XFibrosis_Explicit_Vessel-Fiber_Modeling_for_Fibrosis_Staging_from_Liver_Pathology_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yin_XFibrosis_Explicit_Vessel-Fiber_CVPR_2024_supplemental.pdf | null |
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