Search is not available for this dataset
title
string | authors
string | abstract
string | pdf
string | arXiv
string | bibtex
string | url
string | detail_url
string | tags
string | supp
string | string |
|---|---|---|---|---|---|---|---|---|---|---|
RLHF-V: Towards Trustworthy MLLMs via Behavior Alignment from Fine-grained Correctional Human Feedback | Tianyu Yu, Yuan Yao, Haoye Zhang, Taiwen He, Yifeng Han, Ganqu Cui, Jinyi Hu, Zhiyuan Liu, Hai-Tao Zheng, Maosong Sun, Tat-Seng Chua | Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in multimodal understanding reasoning and interaction. However existing MLLMs prevalently suffer from serious hallucination problems generating text that is not factually grounded in associated images. The problem makes existing MLLMs untrustworthy and thus impractical in real-world (especially high-stakes) applications. To address the challenge we present RLHF-V which enhances MLLM trustworthiness via behavior alignment from fine-grained correctional human feedback. Specifically RLHF-V collects human preference in the form of segment-level corrections on hallucinations and performs dense direct preference optimization over the human feedback. Comprehensive experiments on five benchmarks in both automatic and human evaluation show that RLHF-V can enable substantially more trustworthy MLLM behaviors with promising data and computation efficiency. Remarkably using 1.4k annotated data samples RLHF-V significantly reduces the hallucination rate of the base MLLM by 34.8% outperforming the concurrent LLaVA-RLHF trained on 10k annotated data. The final model achieves state-of-the-art performance in trustworthiness among open-source MLLMs and shows better robustness than GPT-4V in preventing hallucinations aroused from over-generalization. All the data code and model weights will be released to facilitate future research. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_RLHF-V_Towards_Trustworthy_MLLMs_via_Behavior_Alignment_from_Fine-grained_Correctional_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_RLHF-V_Towards_Trustworthy_MLLMs_via_Behavior_Alignment_from_Fine-grained_Correctional_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_RLHF-V_Towards_Trustworthy_MLLMs_via_Behavior_Alignment_from_Fine-grained_Correctional_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_RLHF-V_Towards_Trustworthy_CVPR_2024_supplemental.pdf | null |
ZeroShape: Regression-based Zero-shot Shape Reconstruction | Zixuan Huang, Stefan Stojanov, Anh Thai, Varun Jampani, James M. Rehg | We study the problem of single-image zero-shot 3D shape reconstruction. Recent works learn zero-shot shape reconstruction through generative modeling of 3D assets but these models are computationally expensive at train and inference time. In contrast the traditional approach to this problem is regression-based where deterministic models are trained to directly regress the object shape. Such regression methods possess much higher computational efficiency than generative methods. This raises a natural question: is generative modeling necessary for high performance or conversely are regression-based approaches still competitive? To answer this we design a strong regression-based model called ZeroShape based on the converging findings in this field and a novel insight. We also curate a large real-world evaluation benchmark with objects from three different real-world 3D datasets. This evaluation benchmark is more diverse and an order of magnitude larger than what prior works use to quantitatively evaluate their models aiming at reducing the evaluation variance in our field. We show that ZeroShape not only achieves superior performance over state-of-the-art methods but also demonstrates significantly higher computational and data efficiency. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_ZeroShape_Regression-based_Zero-shot_Shape_Reconstruction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.14198 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_ZeroShape_Regression-based_Zero-shot_Shape_Reconstruction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_ZeroShape_Regression-based_Zero-shot_Shape_Reconstruction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_ZeroShape_Regression-based_Zero-shot_CVPR_2024_supplemental.pdf | null |
Continual-MAE: Adaptive Distribution Masked Autoencoders for Continual Test-Time Adaptation | Jiaming Liu, Ran Xu, Senqiao Yang, Renrui Zhang, Qizhe Zhang, Zehui Chen, Yandong Guo, Shanghang Zhang | Continual Test-Time Adaptation (CTTA) is proposed to migrate a source pre-trained model to continually changing target distributions addressing real-world dynamism. Existing CTTA methods mainly rely on entropy minimization or teacher-student pseudo-labeling schemes for knowledge extraction in unlabeled target domains. However dynamic data distributions cause miscalibrated predictions and noisy pseudo-labels in existing self-supervised learning methods hindering the effective mitigation of error accumulation and catastrophic forgetting problems during the continual adaptation process. To tackle these issues we propose a continual self-supervised method Adaptive Distribution Masked Autoencoders (ADMA) which enhances the extraction of target domain knowledge while mitigating the accumulation of distribution shifts. Specifically we propose a Distribution-aware Masking (DaM) mechanism to adaptively sample masked positions followed by establishing consistency constraints between the masked target samples and the original target samples. Additionally for masked tokens we utilize an efficient decoder to reconstruct a hand-crafted feature descriptor (e.g. Histograms of Oriented Gradients) leveraging its invariant properties to boost task-relevant representations. Through conducting extensive experiments on four widely recognized benchmarks our proposed method attains state-of-the-art performance in both classification and segmentation CTTA tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Continual-MAE_Adaptive_Distribution_Masked_Autoencoders_for_Continual_Test-Time_Adaptation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Continual-MAE_Adaptive_Distribution_Masked_Autoencoders_for_Continual_Test-Time_Adaptation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Continual-MAE_Adaptive_Distribution_Masked_Autoencoders_for_Continual_Test-Time_Adaptation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Continual-MAE_Adaptive_Distribution_CVPR_2024_supplemental.zip | null |
The STVchrono Dataset: Towards Continuous Change Recognition in Time | Yanjun Sun, Yue Qiu, Mariia Khan, Fumiya Matsuzawa, Kenji Iwata | Recognizing continuous changes offers valuable insights into past historical events supports current trend analysis and facilitates future planning. This knowledge is crucial for a variety of fields such as meteorology and agriculture environmental science urban planning and construction tourism and cultural preservation. Currently available datasets in the field of scene change understanding primarily concentrate on two main tasks: the detection of changed regions within a scene and the linguistic description of the change content. Existing datasets focus on recognizing discrete changes such as adding or deleting an object from two images and largely rely on artificially generated images. Consequently the existing change understanding methods primarily focus on identifying distinct object differences overlooking the importance of continuous gradual changes occurring over extended time intervals. To address the above issues we propose a novel benchmark dataset STVchrono targeting the localization and description of long-term continuous changes in real-world scenes. The dataset consists of 71900 photographs from Google Street View API taken over an 18-year span across 50 cities all over the world. Our STVchrono dataset is designed to support real-world continuous change recognition and description in both image pairs and extended image sequences while also enabling the segmentation of changed regions. We conduct experiments to evaluate state-of-the-art methods on continuous change description and segmentation as well as multimodal Large Language Models for describing changes. Our findings reveal that even the most advanced methods lag human performance emphasizing the need to adapt them to continuously changing real-world scenarios. We hope that our benchmark dataset will further facilitate the research of temporal change recognition in a dynamic world. The STVchrono dataset is available at STVchrono Dataset. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sun_The_STVchrono_Dataset_Towards_Continuous_Change_Recognition_in_Time_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_The_STVchrono_Dataset_Towards_Continuous_Change_Recognition_in_Time_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sun_The_STVchrono_Dataset_Towards_Continuous_Change_Recognition_in_Time_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sun_The_STVchrono_Dataset_CVPR_2024_supplemental.pdf | null |
SocialCircle: Learning the Angle-based Social Interaction Representation for Pedestrian Trajectory Prediction | Conghao Wong, Beihao Xia, Ziqian Zou, Yulong Wang, Xinge You | Analyzing and forecasting trajectories of agents like pedestrians and cars in complex scenes has become more and more significant in many intelligent systems and applications. The diversity and uncertainty in socially interactive behaviors among a rich variety of agents make this task more challenging than other deterministic computer vision tasks. Researchers have made a lot of efforts to quantify the effects of these interactions on future trajectories through different mathematical models and network structures but this problem has not been well solved. Inspired by marine animals that localize the positions of their companions underwater through echoes we build a new anglebased trainable social interaction representation named SocialCircle for continuously reflecting the context of social interactions at different angular orientations relative to the target agent. We validate the effect of the proposed SocialCircle by training it along with several newly released trajectory prediction models and experiments show that the SocialCircle not only quantitatively improves the prediction performance but also qualitatively helps better simulate social interactions when forecasting pedestrian trajectories in a way that is consistent with human intuitions. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wong_SocialCircle_Learning_the_Angle-based_Social_Interaction_Representation_for_Pedestrian_Trajectory_CVPR_2024_paper.pdf | http://arxiv.org/abs/2310.05370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wong_SocialCircle_Learning_the_Angle-based_Social_Interaction_Representation_for_Pedestrian_Trajectory_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wong_SocialCircle_Learning_the_Angle-based_Social_Interaction_Representation_for_Pedestrian_Trajectory_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wong_SocialCircle_Learning_the_CVPR_2024_supplemental.pdf | null |
Boosting Neural Representations for Videos with a Conditional Decoder | Xinjie Zhang, Ren Yang, Dailan He, Xingtong Ge, Tongda Xu, Yan Wang, Hongwei Qin, Jun Zhang | Implicit neural representations (INRs) have emerged as a promising approach for video storage and processing showing remarkable versatility across various video tasks. However existing methods often fail to fully leverage their representation capabilities primarily due to inadequate alignment of intermediate features during target frame decoding. This paper introduces a universal boosting framework for current implicit video representation approaches. Specifically we utilize a conditional decoder with a temporal-aware affine transform module which uses the frame index as a prior condition to effectively align intermediate features with target frames. Besides we introduce a sinusoidal NeRV-like block to generate diverse intermediate features and achieve a more balanced parameter distribution thereby enhancing the model's capacity. With a high-frequency information-preserving reconstruction loss our approach successfully boosts multiple baseline INRs in the reconstruction quality and convergence speed for video regression and exhibits superior inpainting and interpolation results. Further we integrate a consistent entropy minimization technique and develop video codecs based on these boosted INRs. Experiments on the UVG dataset confirm that our enhanced codecs significantly outperform baseline INRs and offer competitive rate-distortion performance compared to traditional and learning-based codecs. Code is available at https://github.com/Xinjie-Q/Boosting-NeRV. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Boosting_Neural_Representations_for_Videos_with_a_Conditional_Decoder_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Boosting_Neural_Representations_for_Videos_with_a_Conditional_Decoder_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Boosting_Neural_Representations_for_Videos_with_a_Conditional_Decoder_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Boosting_Neural_Representations_CVPR_2024_supplemental.pdf | null |
Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning | Yutian Luo, Shiqi Zhao, Haoran Wu, Zhiwu Lu | Traditional online class incremental learning assumes class sets in different tasks are disjoint. However recent works have shifted towards a more realistic scenario where tasks have shared classes creating blurred task boundaries. Under this setting although existing approaches could be directly applied challenges like data imbalance and varying class-wise data volumes complicate the critical coreset selection used for replay. To tackle these challenges we introduce DECO (Dual-Enhanced Coreset Selection with Class-wise Collaboration) an approach that starts by establishing a class-wise balanced memory to address data imbalances followed by a tailored class-wise gradient-based similarity scoring system for refined coreset selection strategies with reasonable score guidance to all classes. DECO is distinguished by two main strategies: (1) Collaborative Diverse Score Guidance that mitigates biased knowledge in less-exposed classes through guidance from well-established classes simultaneously consolidating the knowledge in the established classes to enhance overall stability. (2) Adaptive Similarity Score Constraint that relaxes constraints between class types boosting learning plasticity for less-exposed classes and assisting well-established classes in defining clearer boundaries thereby improving overall plasticity. Overall DECO helps effectively identify critical coreset samples improving learning stability and plasticity across all classes. Extensive experiments are conducted on four benchmark datasets to demonstrate the effectiveness and superiority of DECO over other competitors under this online blurry class incremental learning setting. | https://openaccess.thecvf.com/content/CVPR2024/papers/Luo_Dual-Enhanced_Coreset_Selection_with_Class-wise_Collaboration_for_Online_Blurry_Class_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_Dual-Enhanced_Coreset_Selection_with_Class-wise_Collaboration_for_Online_Blurry_Class_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Luo_Dual-Enhanced_Coreset_Selection_with_Class-wise_Collaboration_for_Online_Blurry_Class_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Luo_Dual-Enhanced_Coreset_Selection_CVPR_2024_supplemental.pdf | null |
From Audio to Photoreal Embodiment: Synthesizing Humans in Conversations | Evonne Ng, Javier Romero, Timur Bagautdinov, Shaojie Bai, Trevor Darrell, Angjoo Kanazawa, Alexander Richard | We present a framework for generating full-bodied photorealistic avatars that gesture according to the conversational dynamics of a dyadic interaction. Given speech audio we output multiple possibilities of gestural motion for an individual including face body and hands. The key behind our method is in combining the benefits of sample diversity from vector quantization with the high-frequency details obtained through diffusion to generate more dynamic expressive motion. We visualize the generated motion using highly photorealistic avatars that can express crucial nuances in gestures (e.g. sneers and smirks). To facilitate this line of research we introduce a first-of-its-kind multi-view conversational dataset that allows for photorealistic reconstruction. Experiments show our model generates appropriate and diverse gestures outperforming both diffusion- and VQ-only methods. Furthermore our perceptual evaluation highlights the importance of photorealism (vs. meshes) in accurately assessing subtle motion details in conversational gestures. Code and dataset available on project page. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ng_From_Audio_to_Photoreal_Embodiment_Synthesizing_Humans_in_Conversations_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.01885 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ng_From_Audio_to_Photoreal_Embodiment_Synthesizing_Humans_in_Conversations_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ng_From_Audio_to_Photoreal_Embodiment_Synthesizing_Humans_in_Conversations_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ng_From_Audio_to_CVPR_2024_supplemental.pdf | null |
Single-View Scene Point Cloud Human Grasp Generation | Yan-Kang Wang, Chengyi Xing, Yi-Lin Wei, Xiao-Ming Wu, Wei-Shi Zheng | In this work we explore a novel task of generating human grasps based on single-view scene point clouds which more accurately mirrors the typical real-world situation of observing objects from a single viewpoint. Due to the incompleteness of object point clouds and the presence of numerous scene points the generated hand is prone to penetrating into the invisible parts of the object and the model is easily affected by scene points. Thus we introduce S2HGrasp a framework composed of two key modules: the Global Perception module that globally perceives partial object point clouds and the DiffuGrasp module designed to generate high-quality human grasps based on complex inputs that include scene points. Additionally we introduce S2HGD dataset which comprises approximately 99000 single-object single-view scene point clouds of 1668 unique objects each annotated with one human grasp. Our extensive experiments demonstrate that S2HGrasp can not only generate natural human grasps regardless of scene points but also effectively prevent penetration between the hand and invisible parts of the object. Moreover our model showcases strong generalization capability when applied to unseen objects. Our code and dataset are available at https://github.com/iSEE-Laboratory/S2HGrasp. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Single-View_Scene_Point_Cloud_Human_Grasp_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.15815 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Single-View_Scene_Point_Cloud_Human_Grasp_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Single-View_Scene_Point_Cloud_Human_Grasp_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Single-View_Scene_Point_CVPR_2024_supplemental.pdf | null |
One-step Diffusion with Distribution Matching Distillation | Tianwei Yin, Michaël Gharbi, Richard Zhang, Eli Shechtman, Frédo Durand, William T. Freeman, Taesung Park | Diffusion models generate high-quality images but require dozens of forward passes. We introduce Distribution Matching Distillation (DMD) a procedure to transform a diffusion model into a one-step image generator with minimal impact on image quality. We enforce the one-step image generator match the diffusion model at distribution level by minimizing an approximate KL divergence whose gradient can be expressed as the difference between 2 score functions one of the target distribution and the other of the synthetic distribution being produced by our one-step generator. The score functions are parameterized as two diffusion models trained separately on each distribution. Combined with a simple regression loss matching the large-scale structure of the multi-step diffusion outputs our method outperforms all published few-step diffusion approaches reaching 2.62 FID on ImageNet 64x64 and 11.49 FID on zero-shot COCO-30k comparable to Stable Diffusion but orders of magnitude faster. Utilizing FP16 inference our model can generate images at 20 FPS on modern hardware. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yin_One-step_Diffusion_with_Distribution_Matching_Distillation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18828 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yin_One-step_Diffusion_with_Distribution_Matching_Distillation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yin_One-step_Diffusion_with_Distribution_Matching_Distillation_CVPR_2024_paper.html | CVPR 2024 | null | null |
Cyclic Learning for Binaural Audio Generation and Localization | Zhaojian Li, Bin Zhao, Yuan Yuan | Binaural audio is obtained by simulating the biological structure of human ears which plays an important role in artificial immersive spaces. A promising approach is to utilize mono audio and corresponding vision to synthesize binaural audio thereby avoiding expensive binaural audio recording. However most existing methods directly use the entire scene as a guide ignoring the correspondence between sounds and sounding objects. In this paper we advocate generating binaural audio using fine-grained raw waveform and object-level visual information as guidance. Specifically we propose a Cyclic Locating-and-UPmixing (CLUP) framework that jointly learns visual sounding object localization and binaural audio generation. Visual sounding object localization establishes the correspondence between specific visual objects and sound modalities which provides object-aware guidance to improve binaural generation performance. Meanwhile the spatial information contained in the generated binaural audio can further improve the performance of sounding object localization. In this case visual sounding object localization and binaural audio generation can achieve cyclic learning and benefit from each other. Experimental results demonstrate that on the FAIR-Play benchmark dataset our method is significantly ahead of the existing baselines in multiple evaluation metrics (STFT\downarrow: 0.787 vs. 0.851 ENV\downarrow: 0.128 vs. 0.134 WAV\downarrow: 5.244 vs. 5.684 SNR\uparrow: 7.546 vs. 7.044). | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Cyclic_Learning_for_Binaural_Audio_Generation_and_Localization_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Cyclic_Learning_for_Binaural_Audio_Generation_and_Localization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Cyclic_Learning_for_Binaural_Audio_Generation_and_Localization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_Cyclic_Learning_for_CVPR_2024_supplemental.zip | null |
Neighbor Relations Matter in Video Scene Detection | Jiawei Tan, Hongxing Wang, Jiaxin Li, Zhilong Ou, Zhangbin Qian | Video scene detection aims to temporally link shots for obtaining semantically compact scenes. It is essential for this task to capture scene-distinguishable affinity among shots by similarity assessment. However most methods relies on ordinary shot-to-shot similarities which may inveigle similar shots into being linked even though they are from different scenes and meanwhile hinder dissimilar shots from being blended into a complete scene. In this paper we propose NeighborNet to inject shot contexts into shot-to-shot similarities through carefully exploring the relations between semantic/temporal neighbors of shots over a local time period. In this way shot-to-shot similarities are remeasured as semantic/temporal neighbor-aware similarities so that NeighborNet can learn context embedding into shot features using graph convolutional network. As a result not only do the learned shot features suppress the affinity among similar shots from different scenes but they also promote the affinity among dissimilar shots in the same scene. Experimental results on public benchmark datasets show that our proposed NeighborNet yields substantial improvements in video scene detection especially outperforms released state-of-the-arts by at least 6% in Average Precision (AP). The code is available at https://github.com/ExMorgan-Alter/NeighborNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tan_Neighbor_Relations_Matter_in_Video_Scene_Detection_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Neighbor_Relations_Matter_in_Video_Scene_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tan_Neighbor_Relations_Matter_in_Video_Scene_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tan_Neighbor_Relations_Matter_CVPR_2024_supplemental.pdf | null |
Rethinking Human Motion Prediction with Symplectic Integral | Haipeng Chen, Kedi Lyu, Zhenguang Liu, Yifang Yin, Xun Yang, Yingda Lyu | Long-term and accurate forecasting is the long-standing pursuit of the human motion prediction task. Existing methods typically suffer from dramatic degradation in prediction accuracy with the increasing prediction horizon. It comes down to two reasons:1? Insufficient numerical stability.Unforeseen high noise and complex feature relationships in the data. 2? Inadequate modeling stability. Unreasonable step sizes and undesirable parameter updates in the prediction.In this paper we design a novel and symplectic integral-inspired framework named symplectic integral neural network (SINN) which engages symplectic trajectories to optimize the pose representation and employs a stable symplectic operator to alternately model the dynamic context. Specifically we design a Symplectic Representation Encoder that performs on enhanced human pose representation to obtain trajectories on the symplectic manifold ensuring numerical stability based on Hamiltonian mechanics and symplectic spatial splitting algorithm. We further present the Symplectic Temporal Aggregation module in the light of the symplectic temporal splitting algorithm which splits the long-term prediction into multiple accurate short-term predictions generated by a symplectic operator to secure modeling stability. Moreover our approach is model-agnostic and can be efficiently integrated with different physical dynamics models.The experimental results demonstrate that our method achieves the new state-of-the-art outperforming existing methods by large margins:20.1%on Human3.6M16.7%on CUM Mocap and 10.2% on 3DPW. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Rethinking_Human_Motion_Prediction_with_Symplectic_Integral_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Rethinking_Human_Motion_Prediction_with_Symplectic_Integral_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Rethinking_Human_Motion_Prediction_with_Symplectic_Integral_CVPR_2024_paper.html | CVPR 2024 | null | null |
Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers | Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song | This paper for the first time explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR). We highlight a pivotal discovery: the capacity of text-to-image diffusion models to seamlessly bridge the gap between sketches and photos. This proficiency is underpinned by their robust cross-modal capabilities and shape bias findings that are substantiated through our pilot studies. In order to harness pre-trained diffusion models effectively we introduce a straightforward yet powerful strategy focused on two key aspects: selecting optimal feature layers and utilising visual and textual prompts. For the former we identify which layers are most enriched with information and are best suited for the specific retrieval requirements (category-level or fine-grained). Then we employ visual and textual prompts to guide the model's feature extraction process enabling it to generate more discriminative and contextually relevant cross-modal representations. Extensive experiments on several benchmark datasets validate significant performance improvements. | https://openaccess.thecvf.com/content/CVPR2024/papers/Koley_Text-to-Image_Diffusion_Models_are_Great_Sketch-Photo_Matchmakers_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.07214 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Koley_Text-to-Image_Diffusion_Models_are_Great_Sketch-Photo_Matchmakers_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Koley_Text-to-Image_Diffusion_Models_are_Great_Sketch-Photo_Matchmakers_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Koley_Text-to-Image_Diffusion_Models_CVPR_2024_supplemental.pdf | null |
Mudslide: A Universal Nuclear Instance Segmentation Method | Jun Wang | Nuclear instance segmentation has played a critical role in pathology image analysis. The main challenges arise from the difficulty in accurately segmenting densely overlapping instances and the high cost of precise mask-level annotations. Existing fully-supervised nuclear instance segmentation methods such as boundary-based methods struggle to capture differences between overlapping instances and thus fail in densely distributed blurry regions. They also face challenges transitioning to point supervision where annotations are simple and effective. Inspired by natural mudslides we propose a universal method called Mudslide that uses simple representations to characterize differences between different instances and can easily be extended from fully-supervised to point-supervised. oncretely we introduce a collapse field and leverage it to construct a force map and initial boundary enabling a distinctive representation for each instance. Each pixel is assigned a collapse force with distinct directions between adjacent instances. Starting from the initial boundary Mudslide executes a pixel-by-pixel collapse along various force directions. Pixels that collapse into the same region are considered as one instance concurrently accounting for both inter-instance distinctions and intra-instance coherence. Experiments on public datasets show superior performance in both fully-supervised and point-supervised tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Mudslide_A_Universal_Nuclear_Instance_Segmentation_Method_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Mudslide_A_Universal_Nuclear_Instance_Segmentation_Method_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Mudslide_A_Universal_Nuclear_Instance_Segmentation_Method_CVPR_2024_paper.html | CVPR 2024 | null | null |
CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancement | Qiang Zhu, Jinhua Hao, Yukang Ding, Yu Liu, Qiao Mo, Ming Sun, Chao Zhou, Shuyuan Zhu | Recently numerous approaches have achieved notable success in compressed video quality enhancement (VQE). However these methods usually ignore the utilization of valuable coding priors inherently embedded in compressed videos such as motion vectors and residual frames which carry abundant temporal and spatial information. To remedy this problem we propose the Coding Priors-Guided Aggregation (CPGA) network to utilize temporal and spatial information from coding priors. The CPGA mainly consists of an inter-frame temporal aggregation (ITA) module and a multi-scale non-local aggregation (MNA) module. Specifically the ITA module aggregates temporal information from consecutive frames and coding priors while the MNA module globally captures spatial information guided by residual frames. In addition to facilitate research in VQE task we newly construct the Video Coding Priors (VCP) dataset comprising 300 videos with various coding priors extracted from corresponding bitstreams. It remedies the shortage of previous datasets on the lack of coding information. Experimental results demonstrate the superiority of our method compared to existing state-of-the-art methods. The code and dataset will be released at https://github.com/VQE-CPGA/CPGA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhu_CPGA_Coding_Priors-Guided_Aggregation_Network_for_Compressed_Video_Quality_Enhancement_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.10362 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_CPGA_Coding_Priors-Guided_Aggregation_Network_for_Compressed_Video_Quality_Enhancement_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhu_CPGA_Coding_Priors-Guided_Aggregation_Network_for_Compressed_Video_Quality_Enhancement_CVPR_2024_paper.html | CVPR 2024 | null | null |
MicroCinema: A Divide-and-Conquer Approach for Text-to-Video Generation | Yanhui Wang, Jianmin Bao, Wenming Weng, Ruoyu Feng, Dacheng Yin, Tao Yang, Jingxu Zhang, Qi Dai, Zhiyuan Zhao, Chunyu Wang, Kai Qiu, Yuhui Yuan, Xiaoyan Sun, Chong Luo, Baining Guo | We present MicroCinema a straightforward yet effective framework for high-quality and coherent text-to-video generation. Unlike existing approaches that align text prompts with video directly MicroCinema introduces a Divide-and-Conquer strategy which divides the text-to-video into a two-stage process: text-to-image generation and image&text-to-video generation. This strategy offers two significant advantages. a) It allows us to take full advantage of the recent advances in text-to-image models such as Stable Diffusion Midjourney and DALLE to generate photorealistic and highly detailed images. b) Leveraging the generated image the model can allocate less focus to fine-grained appearance details prioritizing the efficient learning of motion dynamics. To implement this strategy effectively we introduce two core designs. First we propose the Appearance Injection Network enhancing the preservation of the appearance of the given image. Second we introduce the Appearance Noise Prior a novel mechanism aimed at maintaining the capabilities of pre-trained 2D diffusion models. These design elements empower MicroCinema to generate high-quality videos with precise motion guided by the provided text prompts. Extensive experiments demonstrate the superiority of the proposed framework. Concretely MicroCinema achieves SOTA zero-shot FVD of 342.86 on UCF-101 and 377.40 on MSR-VTT. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_MicroCinema_A_Divide-and-Conquer_Approach_for_Text-to-Video_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18829 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_MicroCinema_A_Divide-and-Conquer_Approach_for_Text-to-Video_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_MicroCinema_A_Divide-and-Conquer_Approach_for_Text-to-Video_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_MicroCinema_A_Divide-and-Conquer_CVPR_2024_supplemental.pdf | null |
Learning Instance-Aware Correspondences for Robust Multi-Instance Point Cloud Registration in Cluttered Scenes | Zhiyuan Yu, Zheng Qin, Lintao Zheng, Kai Xu | Multi-instance point cloud registration estimates the poses of multiple instances of a model point cloud in a scene point cloud. Extracting accurate point correspondences is to the center of the problem. Existing approaches usually treat the scene point cloud as a whole overlooking the separation of instances. Therefore point features could be easily polluted by other points from the background or different instances leading to inaccurate correspondences oblivious to separate instances especially in cluttered scenes. In this work we propose MIRETR Multi-Instance REgistration TRansformer a coarse-to-fine approach to the extraction of instance-aware correspondences. At the coarse level it jointly learns instance-aware superpoint features and predicts per-instance masks. With instance masks the influence from outside of the instance being concerned is minimized such that highly reliable superpoint correspondences can be extracted. The superpoint correspondences are then extended to instance candidates at the fine level according to the instance masks. At last an efficient candidate selection and refinement algorithm is devised to obtain the final registrations. Extensive experiments on three public benchmarks demonstrate the efficacy of our approach. In particular MIRETR outperforms the state of the arts by 16.6 points on F1 score on the challenging ROBI benchmark. Code and models are available at https://github.com/zhiyuanYU134/MIRETR | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Learning_Instance-Aware_Correspondences_for_Robust_Multi-Instance_Point_Cloud_Registration_in_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.04557 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Learning_Instance-Aware_Correspondences_for_Robust_Multi-Instance_Point_Cloud_Registration_in_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Learning_Instance-Aware_Correspondences_for_Robust_Multi-Instance_Point_Cloud_Registration_in_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_Learning_Instance-Aware_Correspondences_CVPR_2024_supplemental.pdf | null |
Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting | Haipeng Liu, Yang Wang, Biao Qian, Meng Wang, Yong Rui | Denoising diffusion probabilistic models (DDPMs) for image inpainting aim to add the noise to the texture of the image during the forward process and recover the masked regions with the unmasked ones of the texture via the reverse denoising process. Despite the meaningful semantics generation the existing arts suffer from the semantic discrepancy between the masked and unmasked regions since the semantically dense unmasked texture fails to be completely degraded while the masked regions turn to the pure noise in diffusion process leading to the large discrepancy between them. In this paper we aim to answer how the unmasked semantics guide the texture denoising process; together with how to tackle the semantic discrepancy to facilitate the consistent and meaningful semantics generation. To this end we propose a novel structure-guided diffusion model for image inpainting named StrDiffusion to reformulate the conventional texture denoising process under the structure guidance to derive a simplified denoising objective for image inpainting while revealing: 1) the semantically sparse structure is beneficial to tackle the semantic discrepancy in the early stage while the dense texture generates the reasonable semantics in the late stage; 2) the semantics from the unmasked regions essentially offer the time-dependent structure guidance for the texture denoising process benefiting from the time-dependent sparsity of the structure semantics. For the denoising process a structure-guided neural network is trained to estimate the simplified denoising objective by exploiting the consistency of the denoised structure between masked and unmasked regions. Besides we devise an adaptive resampling strategy as a formal criterion as whether the structure is competent to guide the texture denoising process while regulate their semantic correlations. Extensive experiments validate the merits of StrDiffusion over the state-of-the-arts. Our code is available at https://github.com/htyjers/StrDiffusion. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Structure_Matters_Tackling_the_Semantic_Discrepancy_in_Diffusion_Models_for_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.19898 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Structure_Matters_Tackling_the_Semantic_Discrepancy_in_Diffusion_Models_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Structure_Matters_Tackling_the_Semantic_Discrepancy_in_Diffusion_Models_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Structure_Matters_Tackling_CVPR_2024_supplemental.pdf | null |
Modeling Multimodal Social Interactions: New Challenges and Baselines with Densely Aligned Representations | Sangmin Lee, Bolin Lai, Fiona Ryan, Bikram Boote, James M. Rehg | Understanding social interactions involving both verbal and non-verbal cues is essential for effectively interpreting social situations. However most prior works on multimodal social cues focus predominantly on single-person behaviors or rely on holistic visual representations that are not aligned to utterances in multi-party environments. Consequently they are limited in modeling the intricate dynamics of multi-party interactions. In this paper we introduce three new challenging tasks to model the fine-grained dynamics between multiple people: speaking target identification pronoun coreference resolution and mentioned player prediction. We contribute extensive data annotations to curate these new challenges in social deduction game settings. Furthermore we propose a novel multimodal baseline that leverages densely aligned language-visual representations by synchronizing visual features with their corresponding utterances. This facilitates concurrently capturing verbal and non-verbal cues pertinent to social reasoning. Experiments demonstrate the effectiveness of the proposed approach with densely aligned multimodal representations in modeling fine-grained social interactions. Project website: https://sangmin-git.github.io/projects/MMSI. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lee_Modeling_Multimodal_Social_Interactions_New_Challenges_and_Baselines_with_Densely_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.02090 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_Modeling_Multimodal_Social_Interactions_New_Challenges_and_Baselines_with_Densely_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_Modeling_Multimodal_Social_Interactions_New_Challenges_and_Baselines_with_Densely_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lee_Modeling_Multimodal_Social_CVPR_2024_supplemental.pdf | null |
COCONut: Modernizing COCO Segmentation | Xueqing Deng, Qihang Yu, Peng Wang, Xiaohui Shen, Liang-Chieh Chen | In recent decades the vision community has witnessed remarkable progress in visual recognition partially owing to advancements in dataset benchmarks. Notably the established COCO benchmark has propelled the development of modern detection and segmentation systems. However the COCO segmentation benchmark has seen comparatively slow improvement over the last decade. Originally equipped with coarse polygon annotations for thing instances it gradually incorporated coarse superpixel annotations for stuff regions which were subsequently heuristically amalgamated to yield panoptic segmentation annotations. These annotations executed by different groups of raters have resulted not only in coarse segmentation masks but also in inconsistencies between segmentation types. In this study we undertake a comprehensive reevaluation of the COCO segmentation annotations. By enhancing the annotation quality and expanding the dataset to encompass 383K images with more than 5.18M panoptic masks we introduce COCONut the COCO Next Universal segmenTation dataset. COCONut harmonizes segmentation annotations across semantic instance and panoptic segmentation with meticulously crafted high-quality masks and establishes a robust benchmark for all segmentation tasks. To our knowledge COCONut stands as the inaugural large-scale universal segmentation dataset verified by human raters. We anticipate that the release of COCONut will significantly contribute to the community's ability to assess the progress of novel neural networks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Deng_COCONut_Modernizing_COCO_Segmentation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.08639 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_COCONut_Modernizing_COCO_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_COCONut_Modernizing_COCO_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Deng_COCONut_Modernizing_COCO_CVPR_2024_supplemental.pdf | null |
Semantic Line Combination Detector | Jinwon Ko, Dongkwon Jin, Chang-Su Kim | A novel algorithm called semantic line combination detector (SLCD) to find an optimal combination of semantic lines is proposed in this paper. It processes all lines in each line combination at once to assess the overall harmony of the lines. First we generate various line combinations from reliable lines. Second we estimate the score of each line combination and determine the best one. Experimental results demonstrate that the proposed SLCD outperforms existing semantic line detectors on various datasets. Moreover it is shown that SLCD can be applied effectively to three vision tasks of vanishing point detection symmetry axis detection and composition-based image retrieval. Our codes are available at https://github.com/Jinwon-Ko/SLCD. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ko_Semantic_Line_Combination_Detector_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.18399 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ko_Semantic_Line_Combination_Detector_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ko_Semantic_Line_Combination_Detector_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ko_Semantic_Line_Combination_CVPR_2024_supplemental.pdf | null |
Prompt-Driven Dynamic Object-Centric Learning for Single Domain Generalization | Deng Li, Aming Wu, Yaowei Wang, Yahong Han | Single-domain generalization aims to learn a model from single source domain data attaining generalized performance on other unseen target domains. Existing works primarily focus on improving the generalization ability of static networks. However static networks are unable to dynamically adapt to the diverse variations in different image scenes leading to limited generalization capability. Different scenes exhibit varying levels of complexity and the complexity of images further varies significantly in cross-domain scenarios. In this paper we propose a dynamic object-centric perception network based on prompt learning aiming to adapt to the variations in image complexity. Specifically we propose an object-centric gating module based on prompt learning to focus attention on the object-centric features guided by the various scene prompts. Then with the object-centric gating masks the dynamic selective module dynamically selects highly correlated feature regions in both spatial and channel dimensions enabling the model to adaptively perceive object-centric relevant features thereby enhancing the generalization capability. Extensive experiments were conducted on single-domain generalization tasks in image classification and object detection. The experimental results demonstrate that our approach outperforms state-of-the-art methods which validates the effectiveness and versatility of our proposed method. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_Prompt-Driven_Dynamic_Object-Centric_Learning_for_Single_Domain_Generalization_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18447 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Prompt-Driven_Dynamic_Object-Centric_Learning_for_Single_Domain_Generalization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_Prompt-Driven_Dynamic_Object-Centric_Learning_for_Single_Domain_Generalization_CVPR_2024_paper.html | CVPR 2024 | null | null |
Dual Pose-invariant Embeddings: Learning Category and Object-specific Discriminative Representations for Recognition and Retrieval | Rohan Sarkar, Avinash Kak | In the context of pose-invariant object recognition and retrieval we demonstrate that it is possible to achieve significant improvements in performance if both the category-based and the object-identity-based embeddings are learned simultaneously during training. In hindsight that sounds intuitive because learning about the categories is more fundamental than learning about the individual objects that correspond to those categories. However to the best of what we know no prior work in pose invariant learning has demonstrated this effect. This paper presents an attention-based dual-encoder architecture with specially designed loss functions that optimize the inter- and intra-class distances simultaneously in two different embedding spaces one for the category embeddings and the other for the object level embeddings. The loss functions we have proposed are pose-invariant ranking losses that are designed to minimize the intra-class distances and maximize the inter-class distances in the dual representation spaces. We demonstrate the power of our approach with three challenging multi-view datasets ModelNet-40 ObjectPI and FG3D. With our dual approach for single view object recognition we outperform the previous best by 20.0% on ModelNet40 2.0% on ObjectPI and 46.5% on FG3D. On the other hand for single-view object retrieval we outperform the previous best by 33.7% on ModelNet40 18.8% on ObjectPI and 56.9% on FG3D. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sarkar_Dual_Pose-invariant_Embeddings_Learning_Category_and_Object-specific_Discriminative_Representations_for_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.00272 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sarkar_Dual_Pose-invariant_Embeddings_Learning_Category_and_Object-specific_Discriminative_Representations_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sarkar_Dual_Pose-invariant_Embeddings_Learning_Category_and_Object-specific_Discriminative_Representations_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sarkar_Dual_Pose-invariant_Embeddings_CVPR_2024_supplemental.pdf | null |
vid-TLDR: Training Free Token Merging for Light-weight Video Transformer | Joonmyung Choi, Sanghyeok Lee, Jaewon Chu, Minhyuk Choi, Hyunwoo J. Kim | Video Transformers have become the prevalent solution for various video downstream tasks with superior expressive power and flexibility. However these video transformers suffer from heavy computational costs induced by the massive number of tokens across the entire video frames which has been the major barrier to training the model. Further the patches irrelevant to the main contents e.g. backgrounds degrade the generalization performance of models. To tackle these issues we propose training free token merging for lightweight video Transformer (vid-TLDR) that aims to enhance the efficiency of video Transformers by merging the background tokens without additional training. For vid-TLDR we introduce a novel approach to capture the salient regions in videos only with the attention map. Further we introduce the saliency-aware token merging strategy by dropping the background tokens and sharpening the object scores. Our experiments show that vid-TLDR significantly mitigates the computational complexity of video Transformers while achieving competitive performance compared to the base model without vid-TLDR. Code is available at https://github.com/mlvlab/vid-TLDR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Choi_vid-TLDR_Training_Free_Token_Merging_for_Light-weight_Video_Transformer_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Choi_vid-TLDR_Training_Free_Token_Merging_for_Light-weight_Video_Transformer_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Choi_vid-TLDR_Training_Free_Token_Merging_for_Light-weight_Video_Transformer_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Choi_vid-TLDR_Training_Free_CVPR_2024_supplemental.pdf | null |
DRESS: Instructing Large Vision-Language Models to Align and Interact with Humans via Natural Language Feedback | Yangyi Chen, Karan Sikka, Michael Cogswell, Heng Ji, Ajay Divakaran | We present DRESS a large vision language model (LVLM) that innovatively exploits Natural Language feedback (NLF) from Large Language Models to enhance its alignment and interactions by addressing two key limitations in the state-of-the-art LVLMs. First prior LVLMs generally rely only on the instruction finetuning stage to enhance alignment with human preferences. Without incorporating extra feedback they are still prone to generate unhelpful hallucinated or harmful responses. Second while the visual instruction tuning data is generally structured in a multi-turn dialogue format the connections and dependencies among consecutive conversational turns are weak. This reduces the capacity for effective multi-turn interactions. To tackle these we propose a novel categorization of the NLF into two key types: critique and refinement. The critique NLF identifies the strengths and weaknesses of the responses and is used to align the LVLMs with human preferences. The refinement NLF offers concrete suggestions for improvement and is adopted to improve the interaction ability of the LVLMs-- which focuses on LVLMs' ability to refine responses by incorporating feedback in multi-turn interactions. To address the non-differentiable nature of NLF we generalize conditional reinforcement learning for training. Our experimental results demonstrate that DRESS can generate more helpful (9.76%) honest (11.52%) and harmless (21.03%) responses and more effectively learn from feedback during multi-turn interactions compared to SOTA LVLMs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_DRESS_Instructing_Large_Vision-Language_Models_to_Align_and_Interact_with_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.10081 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_DRESS_Instructing_Large_Vision-Language_Models_to_Align_and_Interact_with_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_DRESS_Instructing_Large_Vision-Language_Models_to_Align_and_Interact_with_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_DRESS_Instructing_Large_CVPR_2024_supplemental.pdf | null |
Makeup Prior Models for 3D Facial Makeup Estimation and Applications | Xingchao Yang, Takafumi Taketomi, Yuki Endo, Yoshihiro Kanamori | In this work we introduce two types of makeup prior models to extend existing 3D face prior models: PCA-based and StyleGAN2-based priors. The PCA-based prior model is a linear model that is easy to construct and is computationally efficient. However it retains only low-frequency information. Conversely the StyleGAN2-based model can represent high-frequency information with relatively higher computational cost than the PCA-based model. Although there is a trade-off between the two models both are applicable to 3D facial makeup estimation and related applications. By leveraging makeup prior models and designing a makeup consistency module we effectively address the challenges that previous methods faced in robustly estimating makeup particularly in the context of handling self-occluded faces. In experiments we demonstrate that our approach reduces computational costs by several orders of magnitude achieving speeds up to 180 times faster. In addition by improving the accuracy of the estimated makeup we confirm that our methods are highly advantageous for various 3D facial makeup applications such as 3D makeup face reconstruction user-friendly makeup editing makeup transfer and interpolation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yang_Makeup_Prior_Models_for_3D_Facial_Makeup_Estimation_and_Applications_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.17761 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Makeup_Prior_Models_for_3D_Facial_Makeup_Estimation_and_Applications_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yang_Makeup_Prior_Models_for_3D_Facial_Makeup_Estimation_and_Applications_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yang_Makeup_Prior_Models_CVPR_2024_supplemental.pdf | null |
Salience DETR: Enhancing Detection Transformer with Hierarchical Salience Filtering Refinement | Xiuquan Hou, Meiqin Liu, Senlin Zhang, Ping Wei, Badong Chen | DETR-like methods have significantly increased detection performance in an end-to-end manner. The mainstream two-stage frameworks of them perform dense self-attention and select a fraction of queries for sparse cross-attention which is proven effective for improving performance but also introduces a heavy computational burden and high dependence on stable query selection. This paper demonstrates that suboptimal two-stage selection strategies result in scale bias and redundancy due to the mismatch between selected queries and objects in two-stage initialization. To address these issues we propose hierarchical salience filtering refinement which performs transformer encoding only on filtered discriminative queries for a better trade-off between computational efficiency and precision. The filtering process overcomes scale bias through a novel scale-independent salience supervision. To compensate for the semantic misalignment among queries we introduce elaborate query refinement modules for stable two-stage initialization. Based on above improvements the proposed Salience DETR achieves significant improvements of +4.0% AP +0.2% AP +4.4% AP on three challenging task-specific detection datasets as well as 49.2% AP on COCO 2017 with less FLOPs. The code is available at https://github.com/xiuqhou/Salience-DETR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hou_Salience_DETR_Enhancing_Detection_Transformer_with_Hierarchical_Salience_Filtering_Refinement_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.16131 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hou_Salience_DETR_Enhancing_Detection_Transformer_with_Hierarchical_Salience_Filtering_Refinement_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hou_Salience_DETR_Enhancing_Detection_Transformer_with_Hierarchical_Salience_Filtering_Refinement_CVPR_2024_paper.html | CVPR 2024 | null | null |
Towards More Unified In-context Visual Understanding | Dianmo Sheng, Dongdong Chen, Zhentao Tan, Qiankun Liu, Qi Chu, Jianmin Bao, Tao Gong, Bin Liu, Shengwei Xu, Nenghai Yu | The rapid advancement of large language models (LLMs) has accelerated the emergence of in-context learning (ICL) as a cutting-edge approach in the natural language processing domain. Recently ICL has been employed in visual understanding tasks such as semantic segmentation and image captioning yielding promising results. However existing visual ICL framework can not enable producing content across multiple modalities which limits their potential usage scenarios. To address this issue we present a new ICL framework for visual understanding with multi-modal output enabled. First we quantize and embed both text and visual prompt into a unified representational space structured as interleaved in-context sequences. Then a decoder-only sparse transformer architecture is employed to perform generative modeling on them facilitating in-context learning. Thanks to this design the model is capable of handling in-context vision understanding tasks with multimodal output in a unified pipeline. Experimental results demonstrate that our model achieves competitive performance compared with specialized models and previous ICL baselines. Overall our research takes a further step toward unified multimodal in-context learning. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sheng_Towards_More_Unified_In-context_Visual_Understanding_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02520 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sheng_Towards_More_Unified_In-context_Visual_Understanding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sheng_Towards_More_Unified_In-context_Visual_Understanding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sheng_Towards_More_Unified_CVPR_2024_supplemental.pdf | null |
F3Loc: Fusion and Filtering for Floorplan Localization | Changan Chen, Rui Wang, Christoph Vogel, Marc Pollefeys | In this paper we propose an efficient data-driven solution to self-localization within a floorplan. Floorplan data is readily available long-term persistent and inherently robust to changes in the visual appearance. Our method does not require retraining per map and location or demand a large database of images of the area of interest. We propose a novel probabilistic model consisting of an observation and a novel temporal filtering module. Operating internally with an efficient ray-based representation the observation module consists of a single and a multiview module to predict horizontal depth from images and fuses their results to benefit from advantages offered by either methodology. Our method operates on conventional consumer hardware and overcomes a common limitation of competing methods that often demand upright images. Our full system meets real-time requirements while outperforming the state-of-the-art by a significant margin. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_F3Loc_Fusion_and_Filtering_for_Floorplan_Localization_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_F3Loc_Fusion_and_Filtering_for_Floorplan_Localization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_F3Loc_Fusion_and_Filtering_for_Floorplan_Localization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_F3Loc_Fusion_and_CVPR_2024_supplemental.pdf | null |
ReconFusion: 3D Reconstruction with Diffusion Priors | Rundi Wu, Ben Mildenhall, Philipp Henzler, Keunhong Park, Ruiqi Gao, Daniel Watson, Pratul P. Srinivasan, Dor Verbin, Jonathan T. Barron, Ben Poole, Aleksander Ho?y?ski | 3D reconstruction methods such as Neural Radiance Fields (NeRFs) excel at rendering photorealistic novel views of complex scenes. However recovering a high-quality NeRF typically requires tens to hundreds of input images resulting in a time-consuming capture process. We present ReconFusion to reconstruct real-world scenes using only a few photos. Our approach leverages a diffusion prior for novel view synthesis trained on synthetic and multiview datasets which regularizes a NeRF-based 3D reconstruction pipeline at novel camera poses beyond those captured by the set of input images. Our method synthesizes realistic geometry and texture in underconstrained regions while preserving the appearance of observed regions. We perform an extensive evaluation across various real-world datasets including forward-facing and 360-degree scenes demonstrating significant performance improvements over previous few-view NeRF reconstruction approaches. Please see our project page at reconfusion.github.io. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_ReconFusion_3D_Reconstruction_with_Diffusion_Priors_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02981 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_ReconFusion_3D_Reconstruction_with_Diffusion_Priors_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_ReconFusion_3D_Reconstruction_with_Diffusion_Priors_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_ReconFusion_3D_Reconstruction_CVPR_2024_supplemental.pdf | null |
I'M HOI: Inertia-aware Monocular Capture of 3D Human-Object Interactions | Chengfeng Zhao, Juze Zhang, Jiashen Du, Ziwei Shan, Junye Wang, Jingyi Yu, Jingya Wang, Lan Xu | We are living in a world surrounded by diverse and "smart" devices with rich modalities of sensing ability. Conveniently capturing the interactions between us humans and these objects remains far-reaching. In this paper we present I'm-HOI a monocular scheme to faithfully capture the 3D motions of both the human and object in a novel setting: using a minimal amount of RGB camera and object-mounted Inertial Measurement Unit (IMU). It combines general motion inference and category-aware refinement. For the former we introduce a holistic human-object tracking method to fuse the IMU signals and the RGB stream and progressively recover the human motions and subsequently the companion object motions. For the latter we tailor a category-aware motion diffusion model which is conditioned on both the raw IMU observations and the results from the previous stage under over-parameterization representation. It significantly refines the initial results and generates vivid body hand and object motions. Moreover we contribute a large dataset with ground truth human and object motions dense RGB inputs and rich object-mounted IMU measurements. Extensive experiments demonstrate the effectiveness of I'm-HOI under a hybrid capture setting. Our dataset and code will be released to the community. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_IM_HOI_Inertia-aware_Monocular_Capture_of_3D_Human-Object_Interactions_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_IM_HOI_Inertia-aware_Monocular_Capture_of_3D_Human-Object_Interactions_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_IM_HOI_Inertia-aware_Monocular_Capture_of_3D_Human-Object_Interactions_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_IM_HOI_Inertia-aware_CVPR_2024_supplemental.pdf | null |
Dynamic Policy-Driven Adaptive Multi-Instance Learning for Whole Slide Image Classification | Tingting Zheng, Kui Jiang, Hongxun Yao | Multi-Instance Learning (MIL) has shown impressive performance for histopathology whole slide image (WSI) analysis using bags or pseudo-bags. It involves instance sampling feature representation and decision-making. However existing MIL-based technologies at least suffer from one or more of the following problems: 1) requiring high storage and intensive pre-processing for numerous instances (sampling); 2) potential over-fitting with limited knowledge to predict bag labels (feature representation); 3) pseudo-bag counts and prior biases affect model robustness and generalizability (decision-making). Inspired by clinical diagnostics using the past sampling instances can facilitate the final WSI analysis but it is barely explored in prior technologies. To break free these limitations we integrate the dynamic instance sampling and reinforcement learning into a unified framework to improve the instance selection and feature aggregation forming a novel Dynamic Policy Instance Selection (DPIS) scheme for better and more credible decision-making. Specifically the measurement of feature distance and reward function are employed to boost continuous instance sampling. To alleviate the over-fitting we explore the latent global relations among instances for more robust and discriminative feature representation while establishing reward and punishment mechanisms to correct biases in pseudo-bags using contrastive learning. These strategies form the final Dynamic Policy-Driven Adaptive Multi-Instance Learning (PAMIL) method for WSI tasks. Extensive experiments reveal that our PAMIL method outperforms the state-of-the-art by 3.8% on CAMELYON16 and 4.4% on TCGA lung cancer datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zheng_Dynamic_Policy-Driven_Adaptive_Multi-Instance_Learning_for_Whole_Slide_Image_Classification_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.07939 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Dynamic_Policy-Driven_Adaptive_Multi-Instance_Learning_for_Whole_Slide_Image_Classification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_Dynamic_Policy-Driven_Adaptive_Multi-Instance_Learning_for_Whole_Slide_Image_Classification_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zheng_Dynamic_Policy-Driven_Adaptive_CVPR_2024_supplemental.pdf | null |
InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks | Zhe Chen, Jiannan Wu, Wenhai Wang, Weijie Su, Guo Chen, Sen Xing, Muyan Zhong, Qinglong Zhang, Xizhou Zhu, Lewei Lu, Bin Li, Ping Luo, Tong Lu, Yu Qiao, Jifeng Dai | The exponential growth of large language models (LLMs) has opened up numerous possibilities for multi-modal AGI systems. However the progress in vision and vision-language foundation models which are also critical elements of multi-modal AGI has not kept pace with LLMs. In this work we design a large-scale vision-language foundation model (InternVL) which scales up the vision foundation model to 6 billion parameters and progressively aligns it with the LLM using web-scale image-text data from various sources. This model can be broadly applied to and achieve state-of-the-art performance on 32 generic visual-linguistic benchmarks including visual perception tasks such as image-level or pixel-level recognition vision-language tasks such as zero-shot image/video classification zero-shot image/video-text retrieval and link with LLMs to create multi-modal dialogue systems. It has powerful visual capabilities and can be a good alternative to the ViT-22B. We hope that our research could contribute to the development of multi-modal large models. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_InternVL_Scaling_up_Vision_Foundation_Models_and_Aligning_for_Generic_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.14238 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_InternVL_Scaling_up_Vision_Foundation_Models_and_Aligning_for_Generic_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_InternVL_Scaling_up_Vision_Foundation_Models_and_Aligning_for_Generic_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_InternVL_Scaling_up_CVPR_2024_supplemental.pdf | null |
Multi-View Attentive Contextualization for Multi-View 3D Object Detection | Xianpeng Liu, Ce Zheng, Ming Qian, Nan Xue, Chen Chen, Zhebin Zhang, Chen Li, Tianfu Wu | We present Multi-View Attentive Contextualization (MvACon) a simple yet effective method for improving 2D-to-3D feature lifting in query-based multi-view 3D (MV3D) object detection. Despite remarkable progress witnessed in the field of query-based MV3D object detection prior art often suffers from either the lack of exploiting high-resolution 2D features in dense attention-based lifting due to high computational costs or from insufficiently dense grounding of 3D queries to multi-scale 2D features in sparse attention-based lifting. Our proposed MvACon hits the two birds with one stone using a representationally dense yet computationally sparse attentive feature contextualization scheme that is agnostic to specific 2D-to-3D feature lifting approaches. In experiments the proposed MvACon is thoroughly tested on the nuScenes benchmark using both the BEVFormer and its recent 3D deformable attention (DFA3D) variant as well as the PETR showing consistent detection performance improvement especially in enhancing performance in location orientation and velocity prediction. It is also tested on the Waymo-mini benchmark using BEVFormer with similar improvement. We qualitatively and quantitatively show that global cluster-based contexts effectively encode dense scene-level contexts for MV3D object detection. The promising results of our proposed MvACon reinforces the adage in computer vision "(contextualized) feature matters". | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Multi-View_Attentive_Contextualization_for_Multi-View_3D_Object_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.12200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Multi-View_Attentive_Contextualization_for_Multi-View_3D_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Multi-View_Attentive_Contextualization_for_Multi-View_3D_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Multi-View_Attentive_Contextualization_CVPR_2024_supplemental.pdf | null |
MemSAM: Taming Segment Anything Model for Echocardiography Video Segmentation | Xiaolong Deng, Huisi Wu, Runhao Zeng, Jing Qin | We propose a novel echocardiographical video segmentation model by adapting SAM to medical videos to address some long-standing challenges in ultrasound video segmentation including (1) massive speckle noise and artifacts (2) extremely ambiguous boundaries and (3) large variations of targeting objects across frames. The core technique of our model is a temporal-aware and noise-resilient prompting scheme. Specifically we employ a space-time memory that contains both spatial and temporal information to prompt the segmentation of current frame and thus we call the proposed model as MemSAM. In prompting the memory carrying temporal cues sequentially prompt the video segmentation frame by frame. Meanwhile as the memory prompt propagates high-level features it avoids the issue of misidentification caused by mask propagation and improves representation consistency. To address the challenge of speckle noise we further propose a memory reinforcement mechanism which leverages predicted masks to improve the quality of the memory before storing it. We extensively evaluate our method on two public datasets and demonstrate state-of-the-art performance compared to existing models. Particularly our model achieves comparable performance with fully supervised approaches with limited annotations. Codes are available at https://github.com/dengxl0520/MemSAM. | https://openaccess.thecvf.com/content/CVPR2024/papers/Deng_MemSAM_Taming_Segment_Anything_Model_for_Echocardiography_Video_Segmentation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_MemSAM_Taming_Segment_Anything_Model_for_Echocardiography_Video_Segmentation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_MemSAM_Taming_Segment_Anything_Model_for_Echocardiography_Video_Segmentation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Deng_MemSAM_Taming_Segment_CVPR_2024_supplemental.pdf | null |
LiDAR4D: Dynamic Neural Fields for Novel Space-time View LiDAR Synthesis | Zehan Zheng, Fan Lu, Weiyi Xue, Guang Chen, Changjun Jiang | Although neural radiance fields (NeRFs) have achieved triumphs in image novel view synthesis (NVS) LiDAR NVS remains largely unexplored. Previous LiDAR NVS methods employ a simple shift from image NVS methods while ignoring the dynamic nature and the large-scale reconstruction problem of LiDAR point clouds. In light of this we propose LiDAR4D a differentiable LiDAR-only framework for novel space-time LiDAR view synthesis. In consideration of the sparsity and large-scale characteristics we design a 4D hybrid representation combined with multi-planar and grid features to achieve effective reconstruction in a coarse-to-fine manner. Furthermore we introduce geometric constraints derived from point clouds to improve temporal consistency. For the realistic synthesis of LiDAR point clouds we incorporate the global optimization of ray-drop probability to preserve cross-region patterns. Extensive experiments on KITTI-360 and NuScenes datasets demonstrate the superiority of our method in accomplishing geometry-aware and time-consistent dynamic reconstruction. Codes are available at https://github.com/ispc-lab/LiDAR4D. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zheng_LiDAR4D_Dynamic_Neural_Fields_for_Novel_Space-time_View_LiDAR_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.02742 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_LiDAR4D_Dynamic_Neural_Fields_for_Novel_Space-time_View_LiDAR_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zheng_LiDAR4D_Dynamic_Neural_Fields_for_Novel_Space-time_View_LiDAR_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zheng_LiDAR4D_Dynamic_Neural_CVPR_2024_supplemental.pdf | null |
Exploiting Diffusion Prior for Generalizable Dense Prediction | Hsin-Ying Lee, Hung-Yu Tseng, Hsin-Ying Lee, Ming-Hsuan Yang | Contents generated by recent advanced Text-to-Image (T2I) diffusion models are sometimes too imaginative for existing off-the-shelf dense predictors to estimate due to the immitigable domain gap. We introduce DMP a pipeline utilizing pre-trained T2I models as a prior for dense prediction tasks. To address the misalignment between deterministic prediction tasks and stochastic T2I models we reformulate the diffusion process through a sequence of interpolations establishing a deterministic mapping between input RGB images and output prediction distributions. To preserve generalizability we use low-rank adaptation to fine-tune pre-trained models. Extensive experiments across five tasks including 3D property estimation semantic segmentation and intrinsic image decomposition showcase the efficacy of the proposed method. Despite limited-domain training data the approach yields faithful estimations for arbitrary images surpassing existing state-of-the-art algorithms. | https://openaccess.thecvf.com/content/CVPR2024/papers/Lee_Exploiting_Diffusion_Prior_for_Generalizable_Dense_Prediction_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.18832 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_Exploiting_Diffusion_Prior_for_Generalizable_Dense_Prediction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Lee_Exploiting_Diffusion_Prior_for_Generalizable_Dense_Prediction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Lee_Exploiting_Diffusion_Prior_CVPR_2024_supplemental.pdf | null |
PI3D: Efficient Text-to-3D Generation with Pseudo-Image Diffusion | Ying-Tian Liu, Yuan-Chen Guo, Guan Luo, Heyi Sun, Wei Yin, Song-Hai Zhang | Diffusion models trained on large-scale text-image datasets have demonstrated a strong capability of controllable high-quality image generation from arbitrary text prompts. However the generation quality and generalization ability of 3D diffusion models is hindered by the scarcity of high-quality and large-scale 3D datasets. In this paper we present PI3D a framework that fully leverages the pre-trained text-to-image diffusion models' ability to generate high-quality 3D shapes from text prompts in minutes. The core idea is to connect the 2D and 3D domains by representing a 3D shape as a set of Pseudo RGB Images. We fine-tune an existing text-to-image diffusion model to produce such pseudo-images using a small number of text-3D pairs. Surprisingly we find that it can already generate meaningful and consistent 3D shapes given complex text descriptions. We further take the generated shapes as the starting point for a lightweight iterative refinement using score distillation sampling to achieve high-quality generation under a low budget. PI3D generates a single 3D shape from text in only 3 minutes and the quality is validated to outperform existing 3D generative models by a large margin. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_PI3D_Efficient_Text-to-3D_Generation_with_Pseudo-Image_Diffusion_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.09069 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_PI3D_Efficient_Text-to-3D_Generation_with_Pseudo-Image_Diffusion_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_PI3D_Efficient_Text-to-3D_Generation_with_Pseudo-Image_Diffusion_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_PI3D_Efficient_Text-to-3D_CVPR_2024_supplemental.zip | null |
Orthogonal Adaptation for Modular Customization of Diffusion Models | Ryan Po, Guandao Yang, Kfir Aberman, Gordon Wetzstein | Customization techniques for text-to-image models have paved the way for a wide range of previously unattainable applications enabling the generation of specific concepts across diverse contexts and styles. While existing methods facilitate high-fidelity customization for individual concepts or a limited pre-defined set of them they fall short of achieving scalability where a single model can seamlessly render countless concepts. In this paper we address a new problem called Modular Customization with the goal of efficiently merging customized models that were fine-tuned independently for individual concepts. This allows the merged model to jointly synthesize concepts in one image without compromising fidelity or incurring any additional computational costs. To address this problem we introduce Orthogonal Adaptation a method designed to encourage the customized models which do not have access to each other during fine-tuning to have orthogonal residual weights. This ensures that during inference time the customized models can be summed with minimal interference. Our proposed method is both simple and versatile applicable to nearly all optimizable weights in the model architecture. Through an extensive set of quantitative and qualitative evaluations our method consistently outperforms relevant baselines in terms of efficiency and identity preservation demonstrating a significant leap toward scalable customization of diffusion models. | https://openaccess.thecvf.com/content/CVPR2024/papers/Po_Orthogonal_Adaptation_for_Modular_Customization_of_Diffusion_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02432 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Po_Orthogonal_Adaptation_for_Modular_Customization_of_Diffusion_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Po_Orthogonal_Adaptation_for_Modular_Customization_of_Diffusion_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Po_Orthogonal_Adaptation_for_CVPR_2024_supplemental.pdf | null |
pixelSplat: 3D Gaussian Splats from Image Pairs for Scalable Generalizable 3D Reconstruction | David Charatan, Sizhe Lester Li, Andrea Tagliasacchi, Vincent Sitzmann | We introduce pixelSplat a feed-forward model that learns to reconstruct 3D radiance fields parameterized by 3D Gaussian primitives from pairs of images. Our model features real-time and memory-efficient rendering for scalable training as well as fast 3D reconstruction at inference time. To overcome local minima inherent to sparse and locally supported representations we predict a dense probability distribution over 3D and sample Gaussian means from that probability distribution. We make this sampling operation differentiable via a reparameterization trick allowing us to back-propagate gradients through the Gaussian splatting representation. We benchmark our method on wide-baseline novel view synthesis on the real-world RealEstate10k and ACID datasets where we outperform state-of-the-art light field transformers and accelerate rendering by 2.5 orders of magnitude while reconstructing an interpretable and editable 3D radiance field. Additional materials can be found on the anonymous project website (pixelsplat.github.io). | https://openaccess.thecvf.com/content/CVPR2024/papers/Charatan_pixelSplat_3D_Gaussian_Splats_from_Image_Pairs_for_Scalable_Generalizable_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.12337 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Charatan_pixelSplat_3D_Gaussian_Splats_from_Image_Pairs_for_Scalable_Generalizable_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Charatan_pixelSplat_3D_Gaussian_Splats_from_Image_Pairs_for_Scalable_Generalizable_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Charatan_pixelSplat_3D_Gaussian_CVPR_2024_supplemental.pdf | null |
VBench: Comprehensive Benchmark Suite for Video Generative Models | Ziqi Huang, Yinan He, Jiashuo Yu, Fan Zhang, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin, Yu Qiao, Ziwei Liu | Video generation has witnessed significant advancements yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable for two reasons: 1) Existing metrics do not fully align with human perceptions; 2) An ideal evaluation system should provide insights to inform future developments of video generation. To this end we present VBench a comprehensive benchmark suite that dissects "video generation quality" into specific hierarchical and disentangled dimensions each with tailored prompts and evaluation methods. VBench has three appealing properties: 1) Comprehensive Dimensions: VBench comprises 16 dimensions in video generation (e.g. subject identity inconsistency motion smoothness temporal flickering and spatial relationship etc). The evaluation metrics with fine-grained levels reveal individual models' strengths and weaknesses. 2) Human Alignment: We also provide a dataset of human preference annotations to validate our benchmarks' alignment with human perception for each evaluation dimension respectively. 3) Valuable Insights: We look into current models' ability across various evaluation dimensions and various content types. We also investigate the gaps between video and image generation models. We will open-source VBench including all prompts evaluation methods generated videos and human preference annotations and also include more video generation models in VBench to drive forward the field of video generation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Huang_VBench_Comprehensive_Benchmark_Suite_for_Video_Generative_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2311.17982 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_VBench_Comprehensive_Benchmark_Suite_for_Video_Generative_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Huang_VBench_Comprehensive_Benchmark_Suite_for_Video_Generative_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Huang_VBench_Comprehensive_Benchmark_CVPR_2024_supplemental.pdf | null |
Language-conditioned Detection Transformer | Jang Hyun Cho, Philipp Krähenbühl | We present a new open-vocabulary detection framework. Our framework uses both image-level labels and detailed detection annotations when available. Our framework proceeds in three steps. We first train a language-conditioned object detector on fully-supervised detection data. This detector gets to see the presence or absence of ground truth classes during training and conditions prediction on the set of present classes. We use this detector to pseudo-label images with image-level labels. Our detector provides much more accurate pseudo-labels than prior approaches with its conditioning mechanism. Finally we train an unconditioned open-vocabulary detector on the pseudo-annotated images. The resulting detector named DECOLA shows strong zero-shot performance in open-vocabulary LVIS benchmark as well as direct zero-shot transfer benchmarks on LVIS COCO Object365 and OpenImages. DECOLA outperforms the prior arts by 17.1 AP-rare and 9.4 mAP on zero-shot LVIS benchmark. DECOLA achieves state-of-the-art results in various model sizes architectures and datasets by only training on open-sourced data and academic-scale computing. Code is available at https://github.com/janghyuncho/DECOLA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cho_Language-conditioned_Detection_Transformer_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cho_Language-conditioned_Detection_Transformer_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cho_Language-conditioned_Detection_Transformer_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Cho_Language-conditioned_Detection_Transformer_CVPR_2024_supplemental.pdf | null |
Optimizing Diffusion Noise Can Serve As Universal Motion Priors | Korrawe Karunratanakul, Konpat Preechakul, Emre Aksan, Thabo Beeler, Supasorn Suwajanakorn, Siyu Tang | We propose Diffusion Noise Optimization (DNO) a new method that effectively leverages existing motion diffusion models as motion priors for a wide range of motion-related tasks. Instead of training a task-specific diffusion model for each new task DNO operates by optimizing the diffusion latent noise of an existing pre-trained text-to-motion model. Given the corresponding latent noise of a human motion it propagates the gradient from the target criteria defined on the motion space through the whole denoising process to update the diffusion latent noise. As a result DNO supports any use cases where criteria can be defined as a function of motion. In particular we show that for motion editing and control DNO outperforms existing methods in both achieving the objective and preserving the motion content. DNO accommodates a diverse range of editing modes including changing trajectory pose joint locations or avoiding newly added obstacles. In addition DNO is effective in motion denoising and completion producing smooth and realistic motion from noisy and partial inputs. DNO achieves these results at inference time without the need for model retraining offering great versatility for any defined reward or loss function on the motion representation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Karunratanakul_Optimizing_Diffusion_Noise_Can_Serve_As_Universal_Motion_Priors_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.11994 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Karunratanakul_Optimizing_Diffusion_Noise_Can_Serve_As_Universal_Motion_Priors_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Karunratanakul_Optimizing_Diffusion_Noise_Can_Serve_As_Universal_Motion_Priors_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Karunratanakul_Optimizing_Diffusion_Noise_CVPR_2024_supplemental.pdf | null |
MAP: MAsk-Pruning for Source-Free Model Intellectual Property Protection | Boyang Peng, Sanqing Qu, Yong Wu, Tianpei Zou, Lianghua He, Alois Knoll, Guang Chen, Changjun Jiang | Deep learning has achieved remarkable progress in various applications heightening the importance of safeguarding the intellectual property (IP) of well-trained models. It entails not only authorizing usage but also ensuring the deployment of models in authorized data domains i.e. making models exclusive to certain target domains. Previous methods necessitate concurrent access to source training data and target unauthorized data when performing IP protection making them risky and inefficient for decentralized private data. In this paper we target a practical setting where only a well-trained source model is available and investigate how we can realize IP protection. To achieve this we propose a novel MAsk Pruning (MAP) framework. MAP stems from an intuitive hypothesis i.e. there are target-related parameters in a well-trained model locating and pruning them is the key to IP protection. Technically MAP freezes the source model and learns a target-specific binary mask to prevent unauthorized data usage while minimizing performance degradation on authorized data. Moreover we introduce a new metric aimed at achieving a better balance between source and target performance degradation. To verify the effectiveness and versatility we have evaluated MAP in a variety of scenarios including vanilla source-available practical source-free and challenging data-free. Extensive experiments indicate that MAP yields new state-of-the-art performance. | https://openaccess.thecvf.com/content/CVPR2024/papers/Peng_MAP_MAsk-Pruning_for_Source-Free_Model_Intellectual_Property_Protection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.04149 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_MAP_MAsk-Pruning_for_Source-Free_Model_Intellectual_Property_Protection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Peng_MAP_MAsk-Pruning_for_Source-Free_Model_Intellectual_Property_Protection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Peng_MAP_MAsk-Pruning_for_CVPR_2024_supplemental.pdf | null |
Improving Single Domain-Generalized Object Detection: A Focus on Diversification and Alignment | Muhammad Sohail Danish, Muhammad Haris Khan, Muhammad Akhtar Munir, M. Saquib Sarfraz, Mohsen Ali | In this work we tackle the problem of domain generalization for object detection specifically focusing on the scenario where only a single source domain is available. We propose an effective approach that involves two key steps: diversifying the source domain and aligning detections based on class prediction confidence and localization. Firstly we demonstrate that by carefully selecting a set of augmentations a base detector can outperform existing methods for single domain generalization by a good margin. This highlights the importance of domain diversification in improving the performance of object detectors. Secondly we introduce a method to align detections from multiple views considering both classification and localization outputs. This alignment procedure leads to better generalized and well-calibrated object detector models which are crucial for accurate decision-making in safety-critical applications. Our approach is detector-agnostic and can be seamlessly applied to both single-stage and two-stage detectors. To validate the effectiveness of our proposed methods we conduct extensive experiments and ablations on challenging domain-shift scenarios. The results consistently demonstrate the superiority of our approach compared to existing methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Danish_Improving_Single_Domain-Generalized_Object_Detection_A_Focus_on_Diversification_and_CVPR_2024_paper.pdf | http://arxiv.org/abs/2405.14497 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Danish_Improving_Single_Domain-Generalized_Object_Detection_A_Focus_on_Diversification_and_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Danish_Improving_Single_Domain-Generalized_Object_Detection_A_Focus_on_Diversification_and_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Danish_Improving_Single_Domain-Generalized_CVPR_2024_supplemental.pdf | null |
OVFoodSeg: Elevating Open-Vocabulary Food Image Segmentation via Image-Informed Textual Representation | Xiongwei Wu, Sicheng Yu, Ee-Peng Lim, Chong-Wah Ngo | In the realm of food computing segmenting ingredients from images poses substantial challenges due to the large intra-class variance among the same ingredients the emergence of new ingredients and the high annotation costs associated with large food segmentation datasets. Existing approaches primarily utilize a closed-vocabulary and static text embeddings setting. These methods often fall short in effectively handling the ingredients particularly new and diverse ones. In response to these limitations we introduce OVFoodSeg a framework that adopts an open-vocabulary setting and enhances text embeddings with visual context. By integrating vision-language models (VLMs) our approach enriches text embedding with image-specific information through two innovative modules e.g. an image-to-text learner FoodLearner and an Image-Informed Text Encoder. The training process of OVFoodSeg is divided into two stages: the pre-training of FoodLearner and the subsequent learning phase for segmentation. The pre-training phase equips FoodLearner with the capability to align visual information with corresponding textual representations that are specifically related to food while the second phase adapts both the FoodLearner and the Image-Informed Text Encoder for the segmentation task. By addressing the deficiencies of previous models OVFoodSeg demonstrates a significant improvement achieving an 4.9% increase in mean Intersection over Union (mIoU) on the FoodSeg103 dataset setting a new milestone for food image segmentation. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_OVFoodSeg_Elevating_Open-Vocabulary_Food_Image_Segmentation_via_Image-Informed_Textual_Representation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01409 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_OVFoodSeg_Elevating_Open-Vocabulary_Food_Image_Segmentation_via_Image-Informed_Textual_Representation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_OVFoodSeg_Elevating_Open-Vocabulary_Food_Image_Segmentation_via_Image-Informed_Textual_Representation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_OVFoodSeg_Elevating_Open-Vocabulary_CVPR_2024_supplemental.pdf | null |
XFeat: Accelerated Features for Lightweight Image Matching | Guilherme Potje, Felipe Cadar, André Araujo, Renato Martins, Erickson R. Nascimento | We introduce a lightweight and accurate architecture for resource-efficient visual correspondence. Our method dubbed XFeat (Accelerated Features) revisits fundamental design choices in convolutional neural networks for detecting extracting and matching local features. Our new model satisfies a critical need for fast and robust algorithms suitable to resource-limited devices. In particular accurate image matching requires sufficiently large image resolutions -- for this reason we keep the resolution as large as possible while limiting the number of channels in the network. Besides our model is designed to offer the choice of matching at the sparse or semi-dense levels each of which may be more suitable for different downstream applications such as visual navigation and augmented reality. Our model is the first to offer semi-dense matching efficiently leveraging a novel match refinement module that relies on coarse local descriptors. XFeat is versatile and hardware-independent surpassing current deep learning-based local features in speed (up to 5x faster) with comparable or better accuracy proven in pose estimation and visual localization. We showcase it running in real-time on an inexpensive laptop CPU without specialized hardware optimizations. Code and weights are available at verlab.dcc.ufmg.br/descriptors/xfeat_cvpr24. | https://openaccess.thecvf.com/content/CVPR2024/papers/Potje_XFeat_Accelerated_Features_for_Lightweight_Image_Matching_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.19174 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Potje_XFeat_Accelerated_Features_for_Lightweight_Image_Matching_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Potje_XFeat_Accelerated_Features_for_Lightweight_Image_Matching_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Potje_XFeat_Accelerated_Features_CVPR_2024_supplemental.pdf | null |
Visual Prompting for Generalized Few-shot Segmentation: A Multi-scale Approach | Mir Rayat Imtiaz Hossain, Mennatullah Siam, Leonid Sigal, James J. Little | The emergence of attention-based transformer models has led to their extensive use in various tasks due to their superior generalization and transfer properties. Recent research has demonstrated that such models when prompted appropriately are excellent for few-shot inference. However such techniques are under-explored for dense prediction tasks like semantic segmentation. In this work we examine the effectiveness of prompting a transformer-decoder with learned visual prompts for the generalized few-shot segmentation (GFSS) task. Our goal is to achieve strong performance not only on novel categories with limited examples but also to retain performance on base categories. We propose an approach to learn visual prompts with limited examples. These learned visual prompts are used to prompt a multiscale transformer decoder to facilitate accurate dense predictions. Additionally we introduce a unidirectional causal attention mechanism between the novel prompts learned with limited examples and the base prompts learned with abundant data. This mechanism enriches the novel prompts without deteriorating the base class performance. Overall this form of prompting helps us achieve state-of-the-art performance for GFSS on two different benchmark datasets: COCO-20^i and Pascal-5^i without the need for test-time optimization (or transduction). Furthermore test-time optimization leveraging unlabelled test data can be used to improve the prompts which we refer to as transductive prompt tuning. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hossain_Visual_Prompting_for_Generalized_Few-shot_Segmentation_A_Multi-scale_Approach_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.11732 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hossain_Visual_Prompting_for_Generalized_Few-shot_Segmentation_A_Multi-scale_Approach_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hossain_Visual_Prompting_for_Generalized_Few-shot_Segmentation_A_Multi-scale_Approach_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hossain_Visual_Prompting_for_CVPR_2024_supplemental.pdf | null |
ARTrackV2: Prompting Autoregressive Tracker Where to Look and How to Describe | Yifan Bai, Zeyang Zhao, Yihong Gong, Xing Wei | We present ARTrackV2 which integrates two pivotal aspects of tracking: determining where to look (localization) and how to describe (appearance analysis) the target object across video frames. Building on the foundation of its predecessor ARTrackV2 extends the concept by introducing a unified generative framework to "read out" object's trajectory and "retell" its appearance in an autoregressive manner. This approach fosters a time-continuous methodology that models the joint evolution of motion and visual features guided by previous estimates. Furthermore ARTrackV2 stands out for its efficiency and simplicity obviating the less efficient intra-frame autoregression and hand-tuned parameters for appearance updates. Despite its simplicity ARTrackV2 achieves state-of-the-art performance on prevailing benchmark datasets while demonstrating a remarkable efficiency improvement. In particular ARTrackV2 achieves an AO score of 79. 5% on GOT-10k and an AUC of 86. 1% on TrackingNet while being 3.6 xfaster than ARTrack. | https://openaccess.thecvf.com/content/CVPR2024/papers/Bai_ARTrackV2_Prompting_Autoregressive_Tracker_Where_to_Look_and_How_to_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.17133 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Bai_ARTrackV2_Prompting_Autoregressive_Tracker_Where_to_Look_and_How_to_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Bai_ARTrackV2_Prompting_Autoregressive_Tracker_Where_to_Look_and_How_to_CVPR_2024_paper.html | CVPR 2024 | null | null |
A Vision Check-up for Language Models | Pratyusha Sharma, Tamar Rott Shaham, Manel Baradad, Stephanie Fu, Adrian Rodriguez-Munoz, Shivam Duggal, Phillip Isola, Antonio Torralba | What does learning to model relationships between strings teach Large Language Models (LLMs) about the visual world? We systematically evaluate LLMs' abilities to generate and recognize an assortment of visual concepts of increasing complexity and then demonstrate how a preliminary visual representation learning system can be trained using models of text. As language models lack the ability to consume or output visual information as pixels we use code to represent images in our study. Although LLM-generated images do not look like natural images results on image generation and the ability of models to correct these generated images indicate that precise modeling of strings can teach language models about numerous aspects of the visual world. Furthermore experiments on self-supervised visual representation learning utilizing images generated with text models highlight the potential to train vision models capable of making semantic assessments of natural images using just LLMs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sharma_A_Vision_Check-up_for_Language_Models_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.01862 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sharma_A_Vision_Check-up_for_Language_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sharma_A_Vision_Check-up_for_Language_Models_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sharma_A_Vision_Check-up_CVPR_2024_supplemental.zip | null |
Memory-based Adapters for Online 3D Scene Perception | Xiuwei Xu, Chong Xia, Ziwei Wang, Linqing Zhao, Yueqi Duan, Jie Zhou, Jiwen Lu | In this paper we propose a new framework for online 3D scene perception. Conventional 3D scene perception methods are offline i.e. take an already reconstructed 3D scene geometry as input which is not applicable in robotic applications where the input data is streaming RGB-D videos rather than a complete 3D scene reconstructed from pre- collected RGB-D videos. To deal with online 3D scene per- ception tasks where data collection and perception should be performed simultaneously the model should be able to process 3D scenes frame by frame and make use of the temporal information. To this end we propose an adapter-based plug-and-play module for the backbone of 3D scene perception model which constructs memory to cache and aggregate the extracted RGB-D features to empower offline models with temporal learning ability. Specifically we propose a queued memory mechanism to cache the supporting point cloud and image features. Then we devise aggregation modules which directly perform on the memory and pass temporal information to current frame. We further propose 3D-to-2D adapter to enhance image features with strong global context. Our adapters can be easily inserted into mainstream offline architectures of different tasks and significantly boost their performance on online tasks. Extensive experiments on ScanNet and SceneNN datasets demonstrate our approach achieves leading performance on three 3D scene perception tasks compared with state-of-the-art online methods by simply finetuning existing offline models without any model and task-specific designs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_Memory-based_Adapters_for_Online_3D_Scene_Perception_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.06974 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Memory-based_Adapters_for_Online_3D_Scene_Perception_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_Memory-based_Adapters_for_Online_3D_Scene_Perception_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_Memory-based_Adapters_for_CVPR_2024_supplemental.pdf | null |
SyncMask: Synchronized Attentional Masking for Fashion-centric Vision-Language Pretraining | Chull Hwan Song, Taebaek Hwang, Jooyoung Yoon, Shunghyun Choi, Yeong Hyeon Gu | Vision-language models (VLMs) have made significant strides in cross-modal understanding through large-scale paired datasets. However in fashion domain datasets often exhibit a disparity between the information conveyed in image and text. This issue stems from datasets containing multiple images of a single fashion item all paired with one text leading to cases where some textual details are not visible in individual images. This mismatch particularly when non-co-occurring elements are masked undermines the training of conventional VLM objectives like Masked Language Modeling and Masked Image Modeling thereby hindering the model's ability to accurately align fine-grained visual and textual features. Addressing this problem we propose Synchronized attentional Masking (SyncMask) which generate masks that pinpoint the image patches and word tokens where the information co-occur in both image and text. This synchronization is accomplished by harnessing cross-attentional features obtained from a momentum model ensuring a precise alignment between the two modalities. Additionally we enhance grouped batch sampling with semi-hard negatives effectively mitigating false negative issues in Image-Text Matching and Image-Text Contrastive learning objectives within fashion datasets. Our experiments demonstrate the effectiveness of the proposed approach outperforming existing methods in three downstream tasks. | https://openaccess.thecvf.com/content/CVPR2024/papers/Song_SyncMask_Synchronized_Attentional_Masking_for_Fashion-centric_Vision-Language_Pretraining_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.01156 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Song_SyncMask_Synchronized_Attentional_Masking_for_Fashion-centric_Vision-Language_Pretraining_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Song_SyncMask_Synchronized_Attentional_Masking_for_Fashion-centric_Vision-Language_Pretraining_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Song_SyncMask_Synchronized_Attentional_CVPR_2024_supplemental.pdf | null |
A Study of Dropout-Induced Modality Bias on Robustness to Missing Video Frames for Audio-Visual Speech Recognition | Yusheng Dai, Hang Chen, Jun Du, Ruoyu Wang, Shihao Chen, Haotian Wang, Chin-Hui Lee | Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames performing even worse than single-modality models. While applying the common dropout techniques to the video modality enhances robustness to missing frames it simultaneously results in a performance loss when dealing with complete data input. In this study we delve into this contrasting phenomenon through the lens of modality bias and uncover that an excessive modality bias towards the audio modality induced by dropout constitutes the fundamental cause. Next we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between the modality bias and the robustness against missing modality in multimodal systems. Building on these findings we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality maintaining performance and robustness simultaneously. Finally to address an entirely missing modality we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated through comprehensive experiments on the MISP2021 and MISP2022 datasets. Our code is available at https://github.com/dalision/ModalBiasAVSR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Dai_A_Study_of_Dropout-Induced_Modality_Bias_on_Robustness_to_Missing_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.04245 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Dai_A_Study_of_Dropout-Induced_Modality_Bias_on_Robustness_to_Missing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Dai_A_Study_of_Dropout-Induced_Modality_Bias_on_Robustness_to_Missing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Dai_A_Study_of_CVPR_2024_supplemental.pdf | null |
A Conditional Denoising Diffusion Probabilistic Model for Point Cloud Upsampling | Wentao Qu, Yuantian Shao, Lingwu Meng, Xiaoshui Huang, Liang Xiao | Point cloud upsampling (PCU) enriches the representation of raw point clouds significantly improving the performance in downstream tasks such as classification and reconstruction. Most of the existing point cloud upsampling methods focus on sparse point cloud feature extraction and upsampling module design. In a different way we dive deeper into directly modelling the gradient of data distribution from dense point clouds. In this paper we proposed a conditional denoising diffusion probabilistic model (DDPM) for point cloud upsampling called PUDM. Specifically PUDM treats the sparse point cloud as a condition and iteratively learns the transformation relationship between the dense point cloud and the noise. Simultaneously PUDM aligns with a dual mapping paradigm to further improve the discernment of point features. In this context PUDM enables learning complex geometry details in the ground truth through the dominant features while avoiding an additional upsampling module design. Furthermore to generate high-quality arbitrary-scale point clouds during inference PUDM exploits the prior knowledge of the scale between sparse point clouds and dense point clouds during training by parameterizing a rate factor. Moreover PUDM exhibits strong noise robustness in experimental results. In the quantitative and qualitative evaluations on PU1K and PUGAN PUDM significantly outperformed existing methods in terms of Chamfer Distance (CD) and Hausdorff Distance (HD) achieving state of the art (SOTA) performance. | https://openaccess.thecvf.com/content/CVPR2024/papers/Qu_A_Conditional_Denoising_Diffusion_Probabilistic_Model_for_Point_Cloud_Upsampling_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.02719 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Qu_A_Conditional_Denoising_Diffusion_Probabilistic_Model_for_Point_Cloud_Upsampling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Qu_A_Conditional_Denoising_Diffusion_Probabilistic_Model_for_Point_Cloud_Upsampling_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Qu_A_Conditional_Denoising_CVPR_2024_supplemental.pdf | null |
VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video Streams | Liao Wang, Kaixin Yao, Chengcheng Guo, Zhirui Zhang, Qiang Hu, Jingyi Yu, Lan Xu, Minye Wu | Neural Radiance Fields (NeRFs) excel in photorealistically rendering static scenes. However rendering dynamic long-duration radiance fields on ubiquitous devices remains challenging due to data storage and computational constraints. In this paper we introduce VideoRF the first approach to enable real-time streaming and rendering of dynamic human-centric radiance fields on mobile platforms. At the core is a serialized 2D feature image stream representing the 4D radiance field all in one. We introduce a tailored training scheme directly applied to this 2D domain to impose the temporal and spatial redundancy of the feature image stream. By leveraging the redundancy we show that the feature image stream can be efficiently compressed by 2D video codecs which allows us to exploit video hardware accelerators to achieve real-time decoding. On the other hand based on the feature image stream we propose a novel rendering pipeline for VideoRF which has specialized space mappings to query radiance properties efficiently. Paired with a deferred shading model VideoRF has the capability of real-time rendering on mobile devices thanks to its efficiency. We have developed a real-time interactive player that enables online streaming and rendering of dynamic scenes offering a seamless and immersive free-viewpoint experience across a range of devices from desktops to mobile phones. Our project page is available at https://aoliao12138.github.io/VideoRF/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_VideoRF_Rendering_Dynamic_Radiance_Fields_as_2D_Feature_Video_Streams_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.01407 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_VideoRF_Rendering_Dynamic_Radiance_Fields_as_2D_Feature_Video_Streams_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_VideoRF_Rendering_Dynamic_Radiance_Fields_as_2D_Feature_Video_Streams_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_VideoRF_Rendering_Dynamic_CVPR_2024_supplemental.pdf | null |
DPHMs: Diffusion Parametric Head Models for Depth-based Tracking | Jiapeng Tang, Angela Dai, Yinyu Nie, Lev Markhasin, Justus Thies, Matthias Nießner | We introduce Diffusion Parametric Head Models (DPHMs) a generative model that enables robust volumetric head reconstruction and tracking from monocular depth sequences. While recent volumetric head models such as NPHMs can now excel in representing high-fidelity head geometries tracking and reconstructing heads from real-world single-view depth sequences remains very challenging as the fitting to partial and noisy observations is underconstrained. To tackle these challenges we propose a latent diffusion-based prior to regularize volumetric head reconstruction and tracking. This prior-based regularizer effectively constrains the identity and expression codes to lie on the underlying latent manifold which represents plausible head shapes. To evaluate the effectiveness of the diffusion-based prior we collect a dataset of monocular Kinect sequences consisting of various complex facial expression motions and rapid transitions. We compare our method to state-of-the-art tracking methods and demonstrate improved head identity reconstruction as well as robust expression tracking. | https://openaccess.thecvf.com/content/CVPR2024/papers/Tang_DPHMs_Diffusion_Parametric_Head_Models_for_Depth-based_Tracking_CVPR_2024_paper.pdf | http://arxiv.org/abs/2312.01068 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_DPHMs_Diffusion_Parametric_Head_Models_for_Depth-based_Tracking_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Tang_DPHMs_Diffusion_Parametric_Head_Models_for_Depth-based_Tracking_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Tang_DPHMs_Diffusion_Parametric_CVPR_2024_supplemental.pdf | null |
DetDiffusion: Synergizing Generative and Perceptive Models for Enhanced Data Generation and Perception | Yibo Wang, Ruiyuan Gao, Kai Chen, Kaiqiang Zhou, Yingjie Cai, Lanqing Hong, Zhenguo Li, Lihui Jiang, Dit-Yan Yeung, Qiang Xu, Kai Zhang | Current perceptive models heavily depend on resource-intensive datasets prompting the need for innovative solutions. Leveraging recent advances in diffusion models synthetic data by constructing image inputs from various annotations proves beneficial for downstream tasks. While prior methods have separately addressed generative and perceptive models DetDiffusion for the first time harmonizes both tackling the challenges in generating effective data for perceptive models. To enhance image generation with perceptive models we introduce perception-aware loss (P.A. loss) through segmentation improving both quality and controllability. To boost the performance of specific perceptive models our method customizes data augmentation by extracting and utilizing perception-aware attribute (P.A. Attr) during generation. Experimental results from the object detection task highlight DetDiffusion's superior performance establishing a new state-of-the-art in layout-guided generation. Furthermore image syntheses from DetDiffusion can effectively augment training data significantly enhancing downstream detection performance. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_DetDiffusion_Synergizing_Generative_and_Perceptive_Models_for_Enhanced_Data_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.13304 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_DetDiffusion_Synergizing_Generative_and_Perceptive_Models_for_Enhanced_Data_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_DetDiffusion_Synergizing_Generative_and_Perceptive_Models_for_Enhanced_Data_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_DetDiffusion_Synergizing_Generative_CVPR_2024_supplemental.pdf | null |
GAFusion: Adaptive Fusing LiDAR and Camera with Multiple Guidance for 3D Object Detection | Xiaotian Li, Baojie Fan, Jiandong Tian, Huijie Fan | Recent years have witnessed the remarkable progress of 3D multi-modality object detection methods based on the Bird's-Eye-View (BEV) perspective. However most of them overlook the complementary interaction and guidance between LiDAR and camera. In this work we propose a novel multi-modality 3D objection detection method named GAFusion with LiDAR-guided global interaction and adaptive fusion. Specifically we introduce sparse depth guidance (SDG) and LiDAR occupancy guidance (LOG) to generate 3D features with sufficient depth information. In the following LiDAR-guided adaptive fusion transformer (LGAFT) is developed to adaptively enhance the interaction of different modal BEV features from a global perspective. Meanwhile additional downsampling with sparse height compression and multi-scale dual-path transformer (MSDPT) are designed to enlarge the receptive fields of different modal features. Finally a temporal fusion module is introduced to aggregate features from previous frames. GAFusion achieves state-of-the-art 3D object detection results with 73.6% mAP and 74.9% NDS on the nuScenes test set. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_GAFusion_Adaptive_Fusing_LiDAR_and_Camera_with_Multiple_Guidance_for_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_GAFusion_Adaptive_Fusing_LiDAR_and_Camera_with_Multiple_Guidance_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_GAFusion_Adaptive_Fusing_LiDAR_and_Camera_with_Multiple_Guidance_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_GAFusion_Adaptive_Fusing_CVPR_2024_supplemental.pdf | null |
Perception-Oriented Video Frame Interpolation via Asymmetric Blending | Guangyang Wu, Xin Tao, Changlin Li, Wenyi Wang, Xiaohong Liu, Qingqing Zheng | Previous methods for Video Frame Interpolation (VFI) have encountered challenges notably the manifestation of blur and ghosting effects. These issues can be traced back to two pivotal factors: unavoidable motion errors and misalignment in supervision. In practice motion estimates often prove to be error-prone resulting in misaligned features. Furthermore the reconstruction loss tends to bring blurry results particularly in misaligned regions. To mitigate these challenges we propose a new paradigm called PerVFI (Perception-oriented Video Frame Interpolation). Our approach incorporates an Asymmetric Synergistic Blending module (ASB) that utilizes features from both sides to synergistically blend intermediate features. One reference frame emphasizes primary content while the other contributes complementary information. To impose a stringent constraint on the blending process we introduce a self-learned sparse quasi-binary mask which effectively mitigates ghosting and blur artifacts in the output. Additionally we employ a normalizing flow-based generator and utilize the negative log-likelihood loss to learn the conditional distribution of the output which further facilitates the generation of clear and fine details. Experimental results validate the superiority of PerVFI demonstrating significant improvements in perceptual quality compared to existing methods. Codes are available at https://github.com/mulns/PerVFI | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Perception-Oriented_Video_Frame_Interpolation_via_Asymmetric_Blending_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.06692 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Perception-Oriented_Video_Frame_Interpolation_via_Asymmetric_Blending_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Perception-Oriented_Video_Frame_Interpolation_via_Asymmetric_Blending_CVPR_2024_paper.html | CVPR 2024 | null | null |
Countering Personalized Text-to-Image Generation with Influence Watermarks | Hanwen Liu, Zhicheng Sun, Yadong Mu | State-of-the-art personalized text-to-image generation systems are usually trained on a few reference images to learn novel visual representations. However this is likely to incur infringement of copyright for the reference image owners when these images are personal and publicly available. Recent progress has been made in protecting these images from unauthorized use by adding protective noises. Yet current protection methods work under the assumption that these protected images are not changed which is in contradiction to the fact that most public platforms intend to modify user-uploaded content e.g. image compression. This paper introduces a robust watermarking method namely InMark to protect images from unauthorized learning. Inspired by influence functions the proposed method forges protective watermarks on more important pixels for these reference images from both heuristic and statistical perspectives. In this way the personal semantics of these images are under protection even if these images are modified to some extent. Extensive experiments demonstrate that the proposed InMark outperforms previous state-of-the-art methods in both protective performance and robustness. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_Countering_Personalized_Text-to-Image_Generation_with_Influence_Watermarks_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Countering_Personalized_Text-to-Image_Generation_with_Influence_Watermarks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_Countering_Personalized_Text-to-Image_Generation_with_Influence_Watermarks_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_Countering_Personalized_Text-to-Image_CVPR_2024_supplemental.pdf | null |
DUDF: Differentiable Unsigned Distance Fields with Hyperbolic Scaling | Miguel Fainstein, Viviana Siless, Emmanuel Iarussi | In recent years there has been a growing interest in training Neural Networks to approximate Unsigned Distance Fields (UDFs) for representing open surfaces in the context of 3D reconstruction. However UDFs are non-differentiable at the zero level set which leads to significant errors in distances and gradients generally resulting in fragmented and discontinuous surfaces. In this paper we propose to learn a hyperbolic scaling of the unsigned distance field which defines a new Eikonal problem with distinct boundary conditions. This allows our formulation to integrate seamlessly with state-of-the-art continuously differentiable implicit neural representation networks largely applied in the literature to represent signed distance fields. Our approach not only addresses the challenge of open surface representation but also demonstrates significant improvement in reconstruction quality and training performance. Moreover the unlocked field's differentiability allows the accurate computation of essential topological properties such as normal directions and curvatures pervasive in downstream tasks such as rendering. Through extensive experiments we validate our approach across various data sets and against competitive baselines. The results demonstrate enhanced accuracy and up to an order of magnitude increase in speed compared to previous methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Fainstein_DUDF_Differentiable_Unsigned_Distance_Fields_with_Hyperbolic_Scaling_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.08876 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Fainstein_DUDF_Differentiable_Unsigned_Distance_Fields_with_Hyperbolic_Scaling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Fainstein_DUDF_Differentiable_Unsigned_Distance_Fields_with_Hyperbolic_Scaling_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Fainstein_DUDF_Differentiable_Unsigned_CVPR_2024_supplemental.pdf | null |
PromptAD: Learning Prompts with only Normal Samples for Few-Shot Anomaly Detection | Xiaofan Li, Zhizhong Zhang, Xin Tan, Chengwei Chen, Yanyun Qu, Yuan Xie, Lizhuang Ma | The vision-language model has brought great improvement to few-shot industrial anomaly detection which usually needs to design of hundreds of prompts through prompt engineering. For automated scenarios we first use conventional prompt learning with many-class paradigm as the baseline to automatically learn prompts but found that it can not work well in one-class anomaly detection. To address the above problem this paper proposes a one-class prompt learning method for few-shot anomaly detection termed PromptAD. First we propose semantic concatenation which can transpose normal prompts into anomaly prompts by concatenating normal prompts with anomaly suffixes thus constructing a large number of negative samples used to guide prompt learning in one-class setting. Furthermore to mitigate the training challenge caused by the absence of anomaly images we introduce the concept of explicit anomaly margin which is used to explicitly control the margin between normal prompt features and anomaly prompt features through a hyper-parameter. For image-level/pixel-level anomaly detection PromptAD achieves first place in 11/12 few-shot settings on MVTec and VisA. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_PromptAD_Learning_Prompts_with_only_Normal_Samples_for_Few-Shot_Anomaly_CVPR_2024_paper.pdf | http://arxiv.org/abs/2404.05231 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_PromptAD_Learning_Prompts_with_only_Normal_Samples_for_Few-Shot_Anomaly_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_PromptAD_Learning_Prompts_with_only_Normal_Samples_for_Few-Shot_Anomaly_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_PromptAD_Learning_Prompts_CVPR_2024_supplemental.pdf | null |
Improving Graph Contrastive Learning via Adaptive Positive Sampling | Jiaming Zhuo, Feiyang Qin, Can Cui, Kun Fu, Bingxin Niu, Mengzhu Wang, Yuanfang Guo, Chuan Wang, Zhen Wang, Xiaochun Cao, Liang Yang | Graph Contrastive Learning (GCL) a Self-Supervised Learning (SSL) architecture tailored for graphs has shown notable potential for mitigating label scarcity. Its core idea is to amplify feature similarities between the positive sample pairs and reduce them between the negative sample pairs. Unfortunately most existing GCLs consistently present suboptimal performances on both homophilic and heterophilic graphs. This is primarily attributed to two limitations of positive sampling that is incomplete local sampling and blind sampling. To address these limitations this paper introduces a novel GCL framework with an adaptive positive sampling module named grapH contrastivE Adaptive posiTive Samples (HEATS). Motivated by the observation that the affinity matrix corresponding to optimal positive sample sets has a block-diagonal structure with equal weights within each block a self-expressive learning objective incorporating the block and idempotent constraint is presented. This learning objective and the contrastive learning objective are iteratively optimized to improve the adaptability and robustness of HEATS. Extensive experiments on graphs and images validate the effectiveness and generality of HEATS. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhuo_Improving_Graph_Contrastive_Learning_via_Adaptive_Positive_Sampling_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhuo_Improving_Graph_Contrastive_Learning_via_Adaptive_Positive_Sampling_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhuo_Improving_Graph_Contrastive_Learning_via_Adaptive_Positive_Sampling_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhuo_Improving_Graph_Contrastive_CVPR_2024_supplemental.pdf | null |
UFC-Net: Unrolling Fixed-point Continuous Network for Deep Compressive Sensing | Xiaoyang Wang, Hongping Gan | Deep unfolding networks (DUNs) renowned for their interpretability and superior performance have invigorated the realm of compressive sensing (CS). Nonetheless existing DUNs frequently suffer from issues related to insufficient feature extraction and feature attrition during the iterative steps. In this paper we propose Unrolling Fixed-point Continuous Network (UFC-Net) a novel deep CS framework motivated by the traditional fixed-point continuous optimization algorithm. Specifically we introduce Convolution-guided Attention Module (CAM) to serve as a critical constituent within the reconstruction phase encompassing tailored components such as Multi-head Attention Residual Block (MARB) Auxiliary Iterative Reconstruction Block (AIRB) etc. MARB effectively integrates multi-head attention mechanisms with convolution to reinforce feature extraction transcending the confinement of localized attributes and facilitating the apprehension of long-range correlations. Meanwhile AIRB introduces auxiliary variables significantly bolstering the preservation of features within each iterative stage. Extensive experiments demonstrate that our proposed UFC-Net achieves remarkable performance both on image CS and CS-magnetic resonance imaging (CS-MRI) in contrast to state-of-the-art methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_UFC-Net_Unrolling_Fixed-point_Continuous_Network_for_Deep_Compressive_Sensing_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_UFC-Net_Unrolling_Fixed-point_Continuous_Network_for_Deep_Compressive_Sensing_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_UFC-Net_Unrolling_Fixed-point_Continuous_Network_for_Deep_Compressive_Sensing_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_UFC-Net_Unrolling_Fixed-point_CVPR_2024_supplemental.pdf | null |
ECoDepth: Effective Conditioning of Diffusion Models for Monocular Depth Estimation | Suraj Patni, Aradhye Agarwal, Chetan Arora | In the absence of parallax cues a learning-based single image depth estimation (SIDE) model relies heavily on shading and contextual cues in the image. While this simplicity is attractive it is necessary to train such models on large and varied datasets which are difficult to capture. It has been shown that using embeddings from pre-trained foundational models such as CLIP improves zero shot transfer in several applications. Taking inspiration from this in our paper we explore the use of global image priors generated from a pre-trained ViT model to provide more detailed contextual information. We argue that the embedding vector from a ViT model pre-trained on a large dataset captures greater relevant information for SIDE than the usual route of generating pseudo image captions followed by CLIP based text embeddings. Based on this idea we propose a new SIDE model using a diffusion backbone which is conditioned on ViT embeddings. Our proposed design establishes a new state-of-the-art (SOTA) for SIDE on NYUv2 dataset achieving Abs Rel error of 0.059(14% improvement) compared to 0.069 by the current SOTA (VPD). And on KITTI dataset achieving Sq Rel error of 0.139 (2% improvement) compared to 0.142 by the current SOTA (GEDepth). For zero-shot transfer with a model trained on NYUv2 we report mean relative improvement of (20% 23% 81% 25%) over NeWCRFs on (Sun-RGBD iBims1 DIODE HyperSim) datasets compared to (16% 18% 45% 9%) by ZoeDepth. The project page is available at https://ecodepth-iitd.github.io | https://openaccess.thecvf.com/content/CVPR2024/papers/Patni_ECoDepth_Effective_Conditioning_of_Diffusion_Models_for_Monocular_Depth_Estimation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18807 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Patni_ECoDepth_Effective_Conditioning_of_Diffusion_Models_for_Monocular_Depth_Estimation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Patni_ECoDepth_Effective_Conditioning_of_Diffusion_Models_for_Monocular_Depth_Estimation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Patni_ECoDepth_Effective_Conditioning_CVPR_2024_supplemental.pdf | null |
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision | Lu Ling, Yichen Sheng, Zhi Tu, Wentian Zhao, Cheng Xin, Kun Wan, Lantao Yu, Qianyu Guo, Zixun Yu, Yawen Lu, Xuanmao Li, Xingpeng Sun, Rohan Ashok, Aniruddha Mukherjee, Hao Kang, Xiangrui Kong, Gang Hua, Tianyi Zhang, Bedrich Benes, Aniket Bera | We have witnessed significant progress in deep learning-based 3D vision ranging from neural radiance field (NeRF) based 3D representation learning to applications in novel view synthesis (NVS). However existing scene-level datasets for deep learning-based 3D vision limited to either synthetic environments or a narrow selection of real-world scenes are quite insufficient. This insufficiency not only hinders a comprehensive benchmark of existing methods but also caps what could be explored in deep learning-based 3D analysis. To address this critical gap we present DL3DV-10K a large-scale scene dataset featuring 51.2 million frames from 10510 videos captured from 65 types of point-of-interest (POI) locations covering both bounded and unbounded scenes with different levels of reflection transparency and lighting. We conducted a comprehensive benchmark of recent NVS methods on DL3DV-10K which revealed valuable insights for future research in NVS. In addition we have obtained encouraging results in a pilot study to learn generalizable NeRF from DL3DV-10K which manifests the necessity of a large-scale scene-level dataset to forge a path toward a foundation model for learning 3D representation. Our DL3DV-10K dataset benchmark results and models will be publicly accessible. | https://openaccess.thecvf.com/content/CVPR2024/papers/Ling_DL3DV-10K_A_Large-Scale_Scene_Dataset_for_Deep_Learning-based_3D_Vision_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Ling_DL3DV-10K_A_Large-Scale_Scene_Dataset_for_Deep_Learning-based_3D_Vision_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Ling_DL3DV-10K_A_Large-Scale_Scene_Dataset_for_Deep_Learning-based_3D_Vision_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Ling_DL3DV-10K_A_Large-Scale_CVPR_2024_supplemental.pdf | null |
2S-UDF: A Novel Two-stage UDF Learning Method for Robust Non-watertight Model Reconstruction from Multi-view Images | Junkai Deng, Fei Hou, Xuhui Chen, Wencheng Wang, Ying He | Recently building on the foundation of neural radiance field various techniques have emerged to learn unsigned distance fields (UDF) to reconstruct 3D non-watertight models from multi-view images. Yet a central challenge in UDF-based volume rendering is formulating a proper way to convert unsigned distance values into volume density ensuring that the resulting weight function remains unbiased and sensitive to occlusions. Falling short on these requirements often results in incorrect topology or large reconstruction errors in resulting models. This paper addresses this challenge by presenting a novel two-stage algorithm 2S-UDF for learning a high-quality UDF from multi-view images. Initially the method applies an easily trainable density function that while slightly biased and transparent aids in coarse reconstruction. The subsequent stage then refines the geometry and appearance of the object to achieve a high-quality reconstruction by directly adjusting the weight function used in volume rendering to ensure that it is unbiased and occlusion-aware. Decoupling density and weight in two stages makes our training stable and robust distinguishing our technique from existing UDF learning approaches. Evaluations on the DeepFashion3D DTU and BlendedMVS datasets validate the robustness and effectiveness of our proposed approach. In both quantitative metrics and visual quality the results indicate our superior performance over other UDF learning techniques in reconstructing 3D non-watertight models from multi-view images. Our code is available at https://bitbucket.org/jkdeng/2sudf/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Deng_2S-UDF_A_Novel_Two-stage_UDF_Learning_Method_for_Robust_Non-watertight_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_2S-UDF_A_Novel_Two-stage_UDF_Learning_Method_for_Robust_Non-watertight_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_2S-UDF_A_Novel_Two-stage_UDF_Learning_Method_for_Robust_Non-watertight_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Deng_2S-UDF_A_Novel_CVPR_2024_supplemental.zip | null |
DETRs Beat YOLOs on Real-time Object Detection | Yian Zhao, Wenyu Lv, Shangliang Xu, Jinman Wei, Guanzhong Wang, Qingqing Dang, Yi Liu, Jie Chen | The YOLO series has become the most popular framework for real-time object detection due to its reasonable trade-off between speed and accuracy. However we observe that the speed and accuracy of YOLOs are negatively affected by the NMS. Recently end-to-end Transformer-based detectors (DETRs) have provided an alternative to eliminating NMS. Nevertheless the high computational cost limits their practicality and hinders them from fully exploiting the advantage of excluding NMS. In this paper we propose the Real-Time DEtection TRansformer (RT-DETR) the first real-time end-to-end object detector to our best knowledge that addresses the above dilemma. We build RT-DETR in two steps drawing on the advanced DETR: first we focus on maintaining accuracy while improving speed followed by maintaining speed while improving accuracy. Specifically we design an efficient hybrid encoder to expeditiously process multi-scale features by decoupling intra-scale interaction and cross-scale fusion to improve speed. Then we propose the uncertainty-minimal query selection to provide high-quality initial queries to the decoder thereby improving accuracy. In addition RT-DETR supports flexible speed tuning by adjusting the number of decoder layers to adapt to various scenarios without retraining. Our RT-DETR-R50 / R101 achieves 53.1% / 54.3% AP on COCO and 108 / 74 FPS on T4 GPU outperforming previously advanced YOLOs in both speed and accuracy. We also develop scaled RT-DETRs that outperform the lighter YOLO detectors (S and M models). Furthermore RT-DETR-R50 outperforms DINO-R50 by 2.2% AP in accuracy and about 21 times in FPS. After pre-training with Objects365 RT-DETR-R50 / R101 achieves 55.3% / 56.2% AP. The project page: https://zhao-yian.github.io/RTDETR. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhao_DETRs_Beat_YOLOs_on_Real-time_Object_Detection_CVPR_2024_paper.pdf | http://arxiv.org/abs/2304.08069 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_DETRs_Beat_YOLOs_on_Real-time_Object_Detection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhao_DETRs_Beat_YOLOs_on_Real-time_Object_Detection_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhao_DETRs_Beat_YOLOs_CVPR_2024_supplemental.pdf | null |
UniVS: Unified and Universal Video Segmentation with Prompts as Queries | Minghan Li, Shuai Li, Xindong Zhang, Lei Zhang | Despite the recent advances in unified image segmentation (IS) developing a unified video segmentation (VS) model remains a challenge. This is mainly because generic category-specified VS tasks need to detect all objects and track them across consecutive frames while prompt-guided VS tasks require re-identifying the target with visual/text prompts throughout the entire video making it hard to handle the different tasks with the same architecture. We make an attempt to address these issues and present a novel unified VS architecture namely UniVS by using prompts as queries. UniVS averages the prompt features of the target from previous frames as its initial query to explicitly decode masks and introduces a target-wise prompt cross-attention layer in the mask decoder to integrate prompt features in the memory pool. By taking the predicted masks of entities from previous frames as their visual prompts UniVS converts different VS tasks into prompt-guided target segmentation eliminating the heuristic inter-frame matching process. Our framework not only unifies the different VS tasks but also naturally achieves universal training and testing ensuring robust performance across different scenarios. UniVS shows a commendable balance between performance and universality on 10 challenging VS benchmarks covering video instance semantic panoptic object and referring segmentation tasks. Code can be found at https://github.com/MinghanLi/UniVS. | https://openaccess.thecvf.com/content/CVPR2024/papers/Li_UniVS_Unified_and_Universal_Video_Segmentation_with_Prompts_as_Queries_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.18115 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Li_UniVS_Unified_and_Universal_Video_Segmentation_with_Prompts_as_Queries_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Li_UniVS_Unified_and_Universal_Video_Segmentation_with_Prompts_as_Queries_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Li_UniVS_Unified_and_CVPR_2024_supplemental.pdf | null |
Bilateral Adaptation for Human-Object Interaction Detection with Occlusion-Robustness | Guangzhi Wang, Yangyang Guo, Ziwei Xu, Mohan Kankanhalli | Human-Object Interaction (HOI) Detection constitutes an important aspect of human-centric scene understanding which requires precise object detection and interaction recognition. Despite increasing advancement in detection recognizing subtle and intricate interactions remains challenging. Recent methods have endeavored to leverage the rich semantic representation from pre-trained CLIP yet fail to efficiently capture finer-grained spatial features that are highly informative for interaction discrimination. In this work instead of solely using representations from CLIP we fill the gap by proposing a spatial adapter that efficiently utilizes the multi-scale spatial information in the pre-trained detector. This leads to a bilateral adaptation that mutually produces complementary features. To further improve interaction recognition under occlusion which is common in crowded scenarios we propose an Occluded Part Extrapolation module that guides the model to recover the spatial details from manually occluded feature maps. Moreover we design a Conditional Contextual Mining module that further mines informative contextual clues from the spatial features via a tailored cross-attention mechanism. Extensive experiments on V-COCO and HICO-DET benchmarks demonstrate that our method significantly outperforms prior art on both standard and zero-shot settings resulting in new state-of-the-art performance. Additional ablation studies further validate the effectiveness of each component in our method. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wang_Bilateral_Adaptation_for_Human-Object_Interaction_Detection_with_Occlusion-Robustness_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Bilateral_Adaptation_for_Human-Object_Interaction_Detection_with_Occlusion-Robustness_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wang_Bilateral_Adaptation_for_Human-Object_Interaction_Detection_with_Occlusion-Robustness_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wang_Bilateral_Adaptation_for_CVPR_2024_supplemental.pdf | null |
An Asymmetric Augmented Self-Supervised Learning Method for Unsupervised Fine-Grained Image Hashing | Feiran Hu, Chenlin Zhang, Jiangliang Guo, Xiu-Shen Wei, Lin Zhao, Anqi Xu, Lingyan Gao | Unsupervised fine-grained image hashing aims to learn compact binary hash codes in unsupervised settings addressing challenges posed by large-scale datasets and dependence on supervision. In this paper we first identify a granularity gap between generic and fine-grained datasets for unsupervised hashing methods highlighting the inadequacy of conventional self-supervised learning for fine-grained visual objects. To bridge this gap we propose the Asymmetric Augmented Self-Supervised Learning (A^2-SSL) method comprising three modules. The asymmetric augmented SSL module employs suitable augmentation strategies for positive/negative views preventing fine-grained category confusion inherent in conventional SSL. Part-oriented dense contrastive learning utilizes the Fisher Vector framework to capture and model fine-grained object parts enhancing unsupervised representations through part-level dense contrastive learning. Self-consistent hash code learning introduces a reconstruction task aligned with the self-consistency principle guiding the model to emphasize comprehensive features particularly fine-grained patterns. Experimental results on five benchmark datasets demonstrate the superiority of A^2-SSL over existing methods affirming its efficacy in unsupervised fine-grained image hashing. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hu_An_Asymmetric_Augmented_Self-Supervised_Learning_Method_for_Unsupervised_Fine-Grained_Image_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_An_Asymmetric_Augmented_Self-Supervised_Learning_Method_for_Unsupervised_Fine-Grained_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hu_An_Asymmetric_Augmented_Self-Supervised_Learning_Method_for_Unsupervised_Fine-Grained_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hu_An_Asymmetric_Augmented_CVPR_2024_supplemental.pdf | null |
Efficiently Assemble Normalization Layers and Regularization for Federated Domain Generalization | Khiem Le, Long Ho, Cuong Do, Danh Le-Phuoc, Kok-Seng Wong | Domain shift is a formidable issue in Machine Learning that causes a model to suffer from performance degradation when tested on unseen domains. Federated Domain Generalization (FedDG) attempts to train a global model using collaborative clients in a privacy-preserving manner that can generalize well to unseen clients possibly with domain shift. However most existing FedDG methods either cause additional privacy risks of data leakage or induce significant costs in client communication and computation which are major concerns in the Federated Learning paradigm. To circumvent these challenges here we introduce a novel architectural method for FedDG namely gPerXAN which relies on a normalization scheme working with a guiding regularizer. In particular we carefully design Personalized eXplicitly Assembled Normalization to enforce client models selectively filtering domain-specific features that are biased towards local data while retaining discrimination of those features. Then we incorporate a simple yet effective regularizer to guide these models in directly capturing domain-invariant representations that the global model's classifier can leverage. Extensive experimental results on two benchmark datasets i.e. PACS and Office-Home and a real-world medical dataset Camelyon17 indicate that our proposed method outperforms other existing methods in addressing this particular problem. | https://openaccess.thecvf.com/content/CVPR2024/papers/Le_Efficiently_Assemble_Normalization_Layers_and_Regularization_for_Federated_Domain_Generalization_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.15605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Le_Efficiently_Assemble_Normalization_Layers_and_Regularization_for_Federated_Domain_Generalization_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Le_Efficiently_Assemble_Normalization_Layers_and_Regularization_for_Federated_Domain_Generalization_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Le_Efficiently_Assemble_Normalization_CVPR_2024_supplemental.pdf | null |
Exploring Pose-Aware Human-Object Interaction via Hybrid Learning | Eastman Z Y Wu, Yali Li, Yuan Wang, Shengjin Wang | Human-Object Interaction (HOI) detection plays a crucial role in visual scene comprehension. In recent advancements two-stage detectors have taken a prominent position. However they are encumbered by two primary challenges. First the misalignment between feature representation and relation reasoning gives rise to a deficiency in discriminative features crucial for interaction detection. Second due to sparse annotation the second-stage interaction head generates numerous candidate <human object> pairs with only a small fraction receiving supervision. Towards these issues we propose a hybrid learning method based on pose-aware HOI feature refinement. Specifically we devise pose-aware feature refinement that encodes spatial features by considering human body pose characteristics. It can direct attention towards key regions ultimately offering a wealth of fine-grained features imperative for HOI detection. Further we introduce a hybrid learning method that combines HOI triplets with probabilistic soft labels supervision which is regenerated from decoupled verb-object pairs. This method explores the implicit connections between the interactions enhancing model generalization without requiring additional data. Our method establishes state-of-the-art performance on HICO-DET benchmark and excels notably in detecting rare HOIs. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wu_Exploring_Pose-Aware_Human-Object_Interaction_via_Hybrid_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Exploring_Pose-Aware_Human-Object_Interaction_via_Hybrid_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wu_Exploring_Pose-Aware_Human-Object_Interaction_via_Hybrid_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wu_Exploring_Pose-Aware_Human-Object_CVPR_2024_supplemental.pdf | null |
Depth Information Assisted Collaborative Mutual Promotion Network for Single Image Dehazing | Yafei Zhang, Shen Zhou, Huafeng Li | Recovering a clear image from a single hazy image is an open inverse problem. Although significant research progress has been made most existing methods ignore the effect that downstream tasks play in promoting upstream dehazing. From the perspective of the haze generation mechanism there is a potential relationship between the depth information of the scene and the hazy image. Based on this we propose a dual-task collaborative mutual promotion framework to achieve the dehazing of a single image. This framework integrates depth estimation and dehazing by a dual-task interaction mechanism and achieves mutual enhancement of their performance. To realize the joint optimization of the two tasks an alternative implementation mechanism with the difference perception is developed. On the one hand the difference perception between the depth maps of the dehazing result and the ideal image is proposed to promote the dehazing network to pay attention to the non-ideal areas of the dehazing. On the other hand by improving the depth estimation performance in the difficult-to-recover areas of the hazy image the dehazing network can explicitly use the depth information of the hazy image to assist the clear image recovery. To promote the depth estimation we propose to use the difference between the dehazed image and the ground truth to guide the depth estimation network to focus on the dehazed unideal areas. It allows dehazing and depth estimation to leverage their strengths in a mutually reinforcing manner. Experimental results show that the proposed method can achieve better performance than that of the state-of-the-art approaches. The source code is released at https://github.com/zhoushen1/DCMPNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhang_Depth_Information_Assisted_Collaborative_Mutual_Promotion_Network_for_Single_Image_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.01105 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Depth_Information_Assisted_Collaborative_Mutual_Promotion_Network_for_Single_Image_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Depth_Information_Assisted_Collaborative_Mutual_Promotion_Network_for_Single_Image_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhang_Depth_Information_Assisted_CVPR_2024_supplemental.pdf | null |
Density-Adaptive Model Based on Motif Matrix for Multi-Agent Trajectory Prediction | Di Wen, Haoran Xu, Zhaocheng He, Zhe Wu, Guang Tan, Peixi Peng | Multi-agent trajectory prediction is essential in autonomous driving risk avoidance and traffic flow control. However the heterogeneous traffic density on interactions which caused by physical laws social norms and so on is often overlooked in existing methods. When the density varies the number of agents involved in interactions and the corresponding interaction probability change dynamically. To tackle this issue we propose a new method called \underline D ensity-\underline A daptive Model based on \underline M otif \underline M atrix for Multi-Agent Trajectory Prediction (DAMM) to gain insights into multi-agent systems. Here we leverage the motif matrix to represent dynamic connectivity in a higher-order pattern and distill the interaction information from the perspectives of the spatial and the temporal dimensions. Specifically in spatial dimension we utilize multi-scale feature fusion to adaptively select the optimal range of neighbors participating in interactions for each time slot. In temporal dimension we extract the temporal interaction features and adapt a pyramidal pooling layer to generate the interaction probability for each agent. Experimental results demonstrate that our approach surpasses state-of-the-art methods on autonomous driving dataset. | https://openaccess.thecvf.com/content/CVPR2024/papers/Wen_Density-Adaptive_Model_Based_on_Motif_Matrix_for_Multi-Agent_Trajectory_Prediction_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Wen_Density-Adaptive_Model_Based_on_Motif_Matrix_for_Multi-Agent_Trajectory_Prediction_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Wen_Density-Adaptive_Model_Based_on_Motif_Matrix_for_Multi-Agent_Trajectory_Prediction_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Wen_Density-Adaptive_Model_Based_CVPR_2024_supplemental.pdf | null |
Contrastive Learning for DeepFake Classification and Localization via Multi-Label Ranking | Cheng-Yao Hong, Yen-Chi Hsu, Tyng-Luh Liu | We propose a unified approach to simultaneously addressing the conventional setting of binary deepfake classification and a more challenging scenario of uncovering what facial components have been forged as well as the exact order of the manipulations. To solve the former task we consider multiple instance learning (MIL) that takes each image as a bag and its patches as instances. A positive bag corresponds to a forged image that includes at least one manipulated patch (i.e. a pixel in the feature map). The formulation allows us to estimate the probability of an input image being a fake one and establish the corresponding contrastive MIL loss. On the other hand tackling the component-wise deepfake problem can be reduced to solving multi-label prediction but the requirement to recover the manipulation order further complicates the learning task into a multi-label ranking problem. We resolve this difficulty by designing a tailor-made loss term to enforce that the rank order of the predicted multi-label probabilities respects the ground-truth order of the sequential modifications of a deepfake image. Through extensive experiments and comparisons with other relevant techniques we provide extensive results and ablation studies to demonstrate that the proposed method is an overall more comprehensive solution to deepfake detection. | https://openaccess.thecvf.com/content/CVPR2024/papers/Hong_Contrastive_Learning_for_DeepFake_Classification_and_Localization_via_Multi-Label_Ranking_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Hong_Contrastive_Learning_for_DeepFake_Classification_and_Localization_via_Multi-Label_Ranking_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Hong_Contrastive_Learning_for_DeepFake_Classification_and_Localization_via_Multi-Label_Ranking_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Hong_Contrastive_Learning_for_CVPR_2024_supplemental.pdf | null |
Unlocking the Potential of Pre-trained Vision Transformers for Few-Shot Semantic Segmentation through Relationship Descriptors | Ziqin Zhou, Hai-Ming Xu, Yangyang Shu, Lingqiao Liu | The recent advent of pre-trained vision transformers has unveiled a promising property: their inherent capability to group semantically related visual concepts. In this paper we explore to harnesses this emergent feature to tackle few-shot semantic segmentation a task focused on classifying pixels in a test image with a few example data. A critical hurdle in this endeavor is preventing overfitting to the limited classes seen during training the few-shot segmentation model. As our main discovery we find that the concept of "relationship descriptors" initially conceived for enhancing the CLIP model for zero-shot semantic segmentation offers a potential solution. We adapt and refine this concept to craft a relationship descriptor construction tailored for few-shot semantic segmentation extending its application across multiple layers to enhance performance. Building upon this adaptation we proposed a few-shot semantic segmentation framework that is not only easy to implement and train but also effectively scales with the number of support examples and categories. Through rigorous experimentation across various datasets including PASCAL-5^ i and COCO-20^ i we demonstrate a clear advantage of our method in diverse few-shot semantic segmentation scenarios and a range of pre-trained vision transformer models. The findings clearly show that our method significantly outperforms current state-of-the-art techniques highlighting the effectiveness of harnessing the emerging capabilities of vision transformers for few-shot semantic segmentation. We release the code at https://github.com/ZiqinZhou66/FewSegwithRD.git. | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_Unlocking_the_Potential_of_Pre-trained_Vision_Transformers_for_Few-Shot_Semantic_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Unlocking_the_Potential_of_Pre-trained_Vision_Transformers_for_Few-Shot_Semantic_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Unlocking_the_Potential_of_Pre-trained_Vision_Transformers_for_Few-Shot_Semantic_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_Unlocking_the_Potential_CVPR_2024_supplemental.pdf | null |
CustomListener: Text-guided Responsive Interaction for User-friendly Listening Head Generation | Xi Liu, Ying Guo, Cheng Zhen, Tong Li, Yingying Ao, Pengfei Yan | Listening head generation aims to synthesize a non-verbal responsive listener head by modeling the correlation between the speaker and the listener in dynamic conversion. The applications of listener agent generation in virtual interaction have promoted many works achieving diverse and fine-grained motion generation. However they can only manipulate motions through simple emotional labels but cannot freely control the listener's motions. Since listener agents should have human-like attributes (e.g. identity personality) which can be freely customized by users this limits their realism. In this paper we propose a user-friendly framework called CustomListener to realize the free-form text prior guided listener generation. To achieve speaker-listener coordination we design a Static to Dynamic Portrait module (SDP) which interacts with speaker information to transform static text into dynamic portrait token with completion rhythm and amplitude information. To achieve coherence between segments we design a Past Guided Generation module (PGG) to maintain the consistency of customized listener attributes through the motion prior and utilize a diffusion-based structure conditioned on the portrait token and the motion prior to realize the controllable generation. To train and evaluate our model we have constructed two text-annotated listening head datasets based on ViCo and RealTalk which provide text-video paired labels. Extensive experiments have verified the effectiveness of our model. | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_CustomListener_Text-guided_Responsive_Interaction_for_User-friendly_Listening_Head_Generation_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.00274 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_CustomListener_Text-guided_Responsive_Interaction_for_User-friendly_Listening_Head_Generation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_CustomListener_Text-guided_Responsive_Interaction_for_User-friendly_Listening_Head_Generation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_CustomListener_Text-guided_Responsive_CVPR_2024_supplemental.pdf | null |
Projecting Trackable Thermal Patterns for Dynamic Computer Vision | Mark Sheinin, Aswin C. Sankaranarayanan, Srinivasa G. Narasimhan | Adding artificial patterns to objects like QR codes can ease tasks such as object tracking robot navigation and conveying information (e.g. a label or a website link). However these patterns require a physical application and they alter the object's appearance. Conversely projected patterns can temporarily change the object's appearance aiding tasks like 3D scanning and retrieving object textures and shading. However projected patterns impede dynamic tasks like object tracking because they do not `stick' to the object's surface. Or do they? This paper introduces a novel approach combining the advantages of projected and persistent physical patterns. Our system projects heat patterns using a laser beam (similar in spirit to a LIDAR) which a thermal camera observes and tracks. Such thermal patterns enable tracking poorly-textured objects whose tracking is highly challenging with standard cameras while not affecting the object's appearance or physical properties. To avail these thermal patterns in existing vision frameworks we train a network to reverse heat diffusion's effects and remove inconsistent pattern points between different thermal frames. We prototyped and tested this approach on dynamic vision tasks like structure from motion optical flow and object tracking of everyday textureless objects. | https://openaccess.thecvf.com/content/CVPR2024/papers/Sheinin_Projecting_Trackable_Thermal_Patterns_for_Dynamic_Computer_Vision_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Sheinin_Projecting_Trackable_Thermal_Patterns_for_Dynamic_Computer_Vision_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Sheinin_Projecting_Trackable_Thermal_Patterns_for_Dynamic_Computer_Vision_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Sheinin_Projecting_Trackable_Thermal_CVPR_2024_supplemental.pdf | null |
SG-PGM: Partial Graph Matching Network with Semantic Geometric Fusion for 3D Scene Graph Alignment and Its Downstream Tasks | Yaxu Xie, Alain Pagani, Didier Stricker | Scene graphs have been recently introduced into 3D spatial understanding as a comprehensive representation of the scene. The alignment between 3D scene graphs is the first step of many downstream tasks such as scene graph aided point cloud registration mosaicking overlap checking and robot navigation. In this work we treat 3D scene graph alignment as a partial graph-matching problem and propose to solve it with a graph neural network. We reuse the geometric features learned by a point cloud registration method and associate the clustered point-level geometric features with the node-level semantic feature via our designed feature fusion module. Partial matching is enabled by using a learnable method to select the top-k similar node pairs. Subsequent downstream tasks such as point cloud registration are achieved by running a pre-trained registration network within the matched regions. We further propose a point-matching rescoring method that uses the node-wise alignment of the 3D scene graph to reweight the matching candidates from a pre-trained point cloud registration method. It reduces the false point correspondences estimated especially in low-overlapping cases. Experiments show that our method improves the alignment accuracy by 10 20% in low-overlap and random transformation scenarios and outperforms the existing work in multiple downstream tasks. Our code and models are available here (https://github.com/dfki-av/sg-pgm.git). | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_SG-PGM_Partial_Graph_Matching_Network_with_Semantic_Geometric_Fusion_for_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_SG-PGM_Partial_Graph_Matching_Network_with_Semantic_Geometric_Fusion_for_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_SG-PGM_Partial_Graph_Matching_Network_with_Semantic_Geometric_Fusion_for_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_SG-PGM_Partial_Graph_CVPR_2024_supplemental.pdf | null |
Fun with Flags: Robust Principal Directions via Flag Manifolds | Nathan Mankovich, Gustau Camps-Valls, Tolga Birdal | Principal component analysis (PCA) along with its extensions to manifolds and outlier contaminated data have been indispensable in computer vision and machine learning. In this work we present a unifying formalism for PCA and its variants and introduce a framework based on the flags of linear subspaces i.e. a hierarchy of nested linear subspaces of increasing dimension which not only allows for a common implementation but also yields novel variants not explored previously. We begin by generalizing traditional PCA methods that either maximize variance or minimize reconstruction error. We expand these interpretations to develop a wide array of new dimensionality reduction algorithms by accounting for outliers and the data manifold. To devise a common computational approach we recast robust and dual forms of PCA as optimization problems on flag manifolds. We then integrate tangent space approximations of principal geodesic analysis (tangent-PCA) into this flag-based framework creating novel robust and dual geodesic PCA variations. The remarkable flexibility offered by the `flagification' introduced here enables even more algorithmic variants identified by specific flag types. Last but not least we propose an effective convergent solver for these flag-formulations employing the Stiefel manifold. Our empirical results on both real-world and synthetic scenarios demonstrate the superiority of our novel algorithms especially in terms of robustness to outliers on manifolds. | https://openaccess.thecvf.com/content/CVPR2024/papers/Mankovich_Fun_with_Flags_Robust_Principal_Directions_via_Flag_Manifolds_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.04071 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Mankovich_Fun_with_Flags_Robust_Principal_Directions_via_Flag_Manifolds_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Mankovich_Fun_with_Flags_Robust_Principal_Directions_via_Flag_Manifolds_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Mankovich_Fun_with_Flags_CVPR_2024_supplemental.pdf | null |
Generating Non-Stationary Textures using Self-Rectification | Yang Zhou, Rongjun Xiao, Dani Lischinski, Daniel Cohen-Or, Hui Huang | This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel two-step approach wherein users first modify a reference texture using standard image editing tools yielding an initial rough target for the synthesis. Subsequently our proposed method termed "self-rectification" automatically refines this target into a coherent seamless texture while faithfully preserving the distinct visual characteristics of the reference exemplar. Our method leverages a pre-trained diffusion network and uses self-attention mechanisms to gradually align the synthesized texture with the reference ensuring the retention of the structures in the provided target. Through experimental validation our approach exhibits exceptional proficiency in handling non-stationary textures demonstrating significant advancements in texture synthesis when compared to existing state-of-the-art techniques. Code is available at https://github.com/xiaorongjun000/Self-Rectification | https://openaccess.thecvf.com/content/CVPR2024/papers/Zhou_Generating_Non-Stationary_Textures_using_Self-Rectification_CVPR_2024_paper.pdf | http://arxiv.org/abs/2401.02847 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Generating_Non-Stationary_Textures_using_Self-Rectification_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhou_Generating_Non-Stationary_Textures_using_Self-Rectification_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Zhou_Generating_Non-Stationary_Textures_CVPR_2024_supplemental.pdf | null |
SPU-PMD: Self-Supervised Point Cloud Upsampling via Progressive Mesh Deformation | Yanzhe Liu, Rong Chen, Yushi Li, Yixi Li, Xuehou Tan | Despite the success of recent upsampling approaches generating high-resolution point sets with uniform distribution and meticulous structures is still challenging. Unlike existing methods that only take spatial information of the raw data into account we regard point cloud upsampling as generating dense point clouds from deformable topology. Motivated by this we present SPU-PMD a self-supervised topological mesh deformation network for 3D densification. As a cascaded framework our architecture is formulated by a series of coarse mesh interpolator and mesh deformers. At each stage the mesh interpolator first produces the initial dense point clouds via mesh interpolation which allows the model to perceive the primitive topology better. Meanwhile the deformer infers the morphing by estimating the movements of mesh nodes and reconstructs the descriptive topology structure. By associating mesh deformation with feature expansion this module progressively refines point clouds' surface uniformity and structural details. To demonstrate the effectiveness of the proposed method extensive quantitative and qualitative experiments are conducted on synthetic and real-scanned 3D data. Also we compare it with state-of-the-art techniques to further illustrate the superiority of our network. The project page is: https://github.com/lyz21/SPU-PMD | https://openaccess.thecvf.com/content/CVPR2024/papers/Liu_SPU-PMD_Self-Supervised_Point_Cloud_Upsampling_via_Progressive_Mesh_Deformation_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_SPU-PMD_Self-Supervised_Point_Cloud_Upsampling_via_Progressive_Mesh_Deformation_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Liu_SPU-PMD_Self-Supervised_Point_Cloud_Upsampling_via_Progressive_Mesh_Deformation_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Liu_SPU-PMD_Self-Supervised_Point_CVPR_2024_supplemental.pdf | null |
Advancing Saliency Ranking with Human Fixations: Dataset Models and Benchmarks | Bowen Deng, Siyang Song, Andrew P. French, Denis Schluppeck, Michael P. Pound | Saliency ranking detection (SRD) has emerged as a challenging task in computer vision aiming not only to identify salient objects within images but also to rank them based on their degree of saliency. Existing SRD datasets have been created primarily using mouse-trajectory data which inadequately captures the intricacies of human visual perception. Addressing this gap this paper introduces the first large-scale SRD dataset SIFR constructed using genuine human fixation data thereby aligning more closely with real visual perceptual processes. To establish a baseline for this dataset we propose QAGNet a novel model that leverages salient instance query features from a transformer detector within a tri-tiered nested graph. Through extensive experiments we demonstrate that our approach outperforms existing state-of-the-art methods across two widely used SRD datasets and our newly proposed dataset. Code and dataset are available at https://github.com/EricDengbowen/QAGNet. | https://openaccess.thecvf.com/content/CVPR2024/papers/Deng_Advancing_Saliency_Ranking_with_Human_Fixations_Dataset_Models_and_Benchmarks_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_Advancing_Saliency_Ranking_with_Human_Fixations_Dataset_Models_and_Benchmarks_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Deng_Advancing_Saliency_Ranking_with_Human_Fixations_Dataset_Models_and_Benchmarks_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Deng_Advancing_Saliency_Ranking_CVPR_2024_supplemental.pdf | null |
Snap Video: Scaled Spatiotemporal Transformers for Text-to-Video Synthesis | Willi Menapace, Aliaksandr Siarohin, Ivan Skorokhodov, Ekaterina Deyneka, Tsai-Shien Chen, Anil Kag, Yuwei Fang, Aleksei Stoliar, Elisa Ricci, Jian Ren, Sergey Tulyakov | Contemporary models for generating images show remarkable quality and versatility. Swayed by these advantages the research community repurposes them to generate videos. Since video content is highly redundant we argue that naively bringing advances of image models to the video generation domain reduces motion fidelity visual quality and impairs scalability. In this work we build Snap Video a video-first model that systematically addresses these challenges. To do that we first extend the EDM framework to take into account spatially and temporally redundant pixels and naturally support video generation. Second we show that a U-Net--a workhorse behind image generation--scales poorly when generating videos requiring significant computational overhead. Hence we propose a new transformer-based architecture that trains 3.31 times faster than U-Nets (and is 4.5 faster at inference). This allows us to efficiently train a text-to-video model with billions of parameters for the first time reach state-of-the-art results on a number of benchmarks and generate videos with substantially higher quality temporal consistency and motion complexity. The user studies showed that our model was favored by a large margin over the most recent methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Menapace_Snap_Video_Scaled_Spatiotemporal_Transformers_for_Text-to-Video_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.14797 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Menapace_Snap_Video_Scaled_Spatiotemporal_Transformers_for_Text-to-Video_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Menapace_Snap_Video_Scaled_Spatiotemporal_Transformers_for_Text-to-Video_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Menapace_Snap_Video_Scaled_CVPR_2024_supplemental.pdf | null |
Unsupervised Deep Unrolling Networks for Phase Unwrapping | Zhile Chen, Yuhui Quan, Hui Ji | Phase unwrapping (PU) is a technique to reconstruct original phase images from their noisy wrapped counterparts finding many applications in scientific imaging. Although supervised learning has shown promise in PU its utility is limited in ground-truth (GT) scarce scenarios. This paper presents an unsupervised learning approach that eliminates the need for GTs during end-to-end training. Our approach leverages the insight that both the gradients and wrapped gradients of wrapped phases serve as noisy labels for GT phase gradients along with sparse outliers induced by the wrapping operation. A recorruption-based self-reconstruction loss in the gradient domain is proposed to mitigate the adverse effects of label noise complemented with a self-distillation loss for improved generalization. Additionally by unfolding a variational model of PU that utilizes wrapped gradients of wrapped phases for its data-fitting term we develop a deep unrolling network that encodes physics of phase wrapping and incorporates special treatments on outliers. In the experiments on three types of phase data our approach outperforms existing GT-free methods and competes well against the supervised ones. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Unsupervised_Deep_Unrolling_Networks_for_Phase_Unwrapping_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Unsupervised_Deep_Unrolling_Networks_for_Phase_Unwrapping_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Unsupervised_Deep_Unrolling_Networks_for_Phase_Unwrapping_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Unsupervised_Deep_Unrolling_CVPR_2024_supplemental.pdf | null |
Federated Generalized Category Discovery | Nan Pu, Wenjing Li, Xingyuan Ji, Yalan Qin, Nicu Sebe, Zhun Zhong | Generalized category discovery (GCD) aims at grouping unlabeled samples from known and unknown classes given labeled data of known classes. To meet the recent decentralization trend in the community we introduce a practical yet challenging task Federated GCD (Fed-GCD) where the training data are distributed in local clients and cannot be shared among clients. Fed-GCD aims to train a generic GCD model by client collaboration under the privacy-protected constraint. The Fed-GCD leads to two challenges: 1) representation degradation caused by training each client model with fewer data than centralized GCD learning and 2) highly heterogeneous label spaces across different clients. To this end we propose a novel Associated Gaussian Contrastive Learning (AGCL) framework based on learnable GMMs which consists of a Client Semantics Association (CSA) and a global-local GMM Contrastive Learning (GCL). On the server CSA aggregates the heterogeneous categories of local-client GMMs to generate a global GMM containing more comprehensive category knowledge. On each client GCL builds class-level contrastive learning with both local and global GMMs. The local GCL learns robust representation with limited local data. The global GCL encourages the model to produce more discriminative representation with the comprehensive category relationships that may not exist in local data. We build a benchmark based on six visual datasets to facilitate the study of Fed-GCD. Extensive experiments show that our AGCL outperforms multiple baselines on all datasets. | https://openaccess.thecvf.com/content/CVPR2024/papers/Pu_Federated_Generalized_Category_Discovery_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Pu_Federated_Generalized_Category_Discovery_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Pu_Federated_Generalized_Category_Discovery_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Pu_Federated_Generalized_Category_CVPR_2024_supplemental.zip | null |
JointSQ: Joint Sparsification-Quantization for Distributed Learning | Weiying Xie, Haowei Li, Jitao Ma, Yunsong Li, Jie Lei, Donglai Liu, Leyuan Fang | Gradient sparsification and quantization offer a promising prospect to alleviate the communication overhead problem in distributed learning. However direct combination of the two results in suboptimal solutions due to the fact that sparsification and quantization haven't been learned together. In this paper we propose Joint Sparsification-Quantization (JointSQ) inspired by the discovery that sparsification can be treated as 0-bit quantization regardless of architectures. Specifically we mathematically formulate JointSQ as a mixed-precision quantization problem expanding the solution space. It can be solved by the designed MCKP-Greedy algorithm. Theoretical analysis demonstrates the minimal compression noise of JointSQ and extensive experiments on various network architectures including CNN RNN and Transformer also validate this point. Under the introduction of computation overhead consistent with or even lower than previous methods JointSQ achieves a compression ratio of 1000xon different models while maintaining near-lossless accuracy and brings 1.4xto 2.9xspeedup over existing methods. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xie_JointSQ_Joint_Sparsification-Quantization_for_Distributed_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_JointSQ_Joint_Sparsification-Quantization_for_Distributed_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xie_JointSQ_Joint_Sparsification-Quantization_for_Distributed_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xie_JointSQ_Joint_Sparsification-Quantization_CVPR_2024_supplemental.pdf | null |
A Unified Framework for Human-centric Point Cloud Video Understanding | Yiteng Xu, Kecheng Ye, Xiao Han, Yiming Ren, Xinge Zhu, Yuexin Ma | Human-centric Point Cloud Video Understanding (PVU) is an emerging field focused on extracting and interpreting human-related features from sequences of human point clouds further advancing downstream human-centric tasks and applications. Previous works usually focus on tackling one specific task and rely on huge labeled data which has poor generalization capability. Considering that human has specific characteristics including the structural semantics of human body and the dynamics of human motions we propose a unified framework to make full use of the prior knowledge and explore the inherent features in the data itself for generalized human-centric point cloud video understanding. Extensive experiments demonstrate that our method achieves state-of-the-art performance on various human-related tasks including action recognition and 3D pose estimation. All datasets and code will be released soon. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xu_A_Unified_Framework_for_Human-centric_Point_Cloud_Video_Understanding_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.20031 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_A_Unified_Framework_for_Human-centric_Point_Cloud_Video_Understanding_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xu_A_Unified_Framework_for_Human-centric_Point_Cloud_Video_Understanding_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xu_A_Unified_Framework_CVPR_2024_supplemental.pdf | null |
Edge-Aware 3D Instance Segmentation Network with Intelligent Semantic Prior | Wonseok Roh, Hwanhee Jung, Giljoo Nam, Jinseop Yeom, Hyunje Park, Sang Ho Yoon, Sangpil Kim | While recent 3D instance segmentation approaches show promising results based on transformer architectures they often fail to correctly identify instances with similar appearances. They also ambiguously determine edges leading to multiple misclassifications of adjacent edge points. In this work we introduce a novel framework called EASE to overcome these challenges and improve the perception of complex 3D instances. We first propose a semantic guidance network to leverage rich semantic knowledge from a language model as intelligent priors enhancing the functional understanding of real-world instances beyond relying solely on geometrical information. We explicitly instruct the basic instance queries using text embeddings of each instance to learn deep semantic details. Further we utilize the edge prediction module encouraging the segmentation network to be edge-aware. We extract voxel-wise edge maps from point features and use them as auxiliary information for learning edge cues. In our extensive experiments on large-scale benchmarks ScanNetV2 ScanNet200 S3DIS and STPLS3D our EASE outperforms existing state-of-the-art models demonstrating its superior performance. | https://openaccess.thecvf.com/content/CVPR2024/papers/Roh_Edge-Aware_3D_Instance_Segmentation_Network_with_Intelligent_Semantic_Prior_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Roh_Edge-Aware_3D_Instance_Segmentation_Network_with_Intelligent_Semantic_Prior_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Roh_Edge-Aware_3D_Instance_Segmentation_Network_with_Intelligent_Semantic_Prior_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Roh_Edge-Aware_3D_Instance_CVPR_2024_supplemental.pdf | null |
Coherence As Texture - Passive Textureless 3D Reconstruction by Self-interference | Wei-Yu Chen, Aswin C. Sankaranarayanan, Anat Levin, Matthew O'Toole | Passive depth estimation based on stereo or defocus relies on the presence of the texture on an object to resolve its depth. Hence recovering the depth of a textureless object-- for example a large white wall--is not just hard but perhaps even impossible. Or is it? We show that spatial coherence a property of natural light sources can be used to resolve the depth of a scene point even when it is textureless. Our approach relies on the idea that natural light scattered off a scene point is locally coherent with itself while incoherent with the light scattered from other surface points; we use this insight to design an optical setup that uses self-interference as a texture feature for estimating depth. Our lab prototype is capable of resolving the depths of textureless objects in sunlight as well as indoor lights. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Coherence_As_Texture_-_Passive_Textureless_3D_Reconstruction_by_Self-interference_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Coherence_As_Texture_-_Passive_Textureless_3D_Reconstruction_by_Self-interference_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Coherence_As_Texture_-_Passive_Textureless_3D_Reconstruction_by_Self-interference_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Coherence_As_Texture_CVPR_2024_supplemental.pdf | null |
Enhancing the Power of OOD Detection via Sample-Aware Model Selection | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Xue_Enhancing_the_Power_of_OOD_Detection_via_Sample-Aware_Model_Selection_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xue_Enhancing_the_Power_of_OOD_Detection_via_Sample-Aware_Model_Selection_CVPR_2024_paper.html | CVPR 2024 | null | null |
Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles | Rui Song, Chenwei Liang, Hu Cao, Zhiran Yan, Walter Zimmer, Markus Gross, Andreas Festag, Alois Knoll | Collaborative perception in automated vehicles leverages the exchange of information between agents aiming to elevate perception results. Previous camera-based collaborative 3D perception methods typically employ 3D bounding boxes or bird's eye views as representations of the environment. However these approaches fall short in offering a comprehensive 3D environmental prediction. To bridge this gap we introduce the first method for collaborative 3D semantic occupancy prediction. Particularly it improves local 3D semantic occupancy predictions by hybrid fusion of (i) semantic and occupancy task features and (ii) compressed orthogonal attention features shared between vehicles. Additionally due to the lack of a collaborative perception dataset designed for semantic occupancy prediction we augment a current collaborative perception dataset to include 3D collaborative semantic occupancy labels for a more robust evaluation. The experimental findings highlight that: (i) our collaborative semantic occupancy predictions excel above the results from single vehicles by over 30% and (ii) models anchored on semantic occupancy outpace state-of-the-art collaborative 3D detection techniques in subsequent perception applications showcasing enhanced accuracy and enriched semantic-awareness in road environments. | https://openaccess.thecvf.com/content/CVPR2024/papers/Song_Collaborative_Semantic_Occupancy_Prediction_with_Hybrid_Feature_Fusion_in_Connected_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.07635 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Song_Collaborative_Semantic_Occupancy_Prediction_with_Hybrid_Feature_Fusion_in_Connected_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Song_Collaborative_Semantic_Occupancy_Prediction_with_Hybrid_Feature_Fusion_in_Connected_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Song_Collaborative_Semantic_Occupancy_CVPR_2024_supplemental.pdf | null |
Generative Multi-modal Models are Good Class Incremental Learners | Xusheng Cao, Haori Lu, Linlan Huang, Xialei Liu, Ming-Ming Cheng | In class incremental learning (CIL) scenarios the phenomenon of catastrophic forgetting caused by the classifier's bias towards the current task has long posed a significant challenge. It is mainly caused by the characteristic of discriminative models. With the growing popularity of the generative multi-modal models we would explore replacing discriminative models with generative ones for CIL. However transitioning from discriminative to generative models requires addressing two key challenges. The primary challenge lies in transferring the generated textual information into the classification of distinct categories. Additionally it requires formulating the task of CIL within a generative framework. To this end we propose a novel generative multi-modal model (GMM) framework for class incremental learning. Our approach directly generates labels for images using an adapted generative model. After obtaining the detailed text we use a text encoder to extract text features and employ feature matching to determine the most similar label as the classification prediction. In the conventional CIL settings we achieve significantly better results in long-sequence task scenarios. Under the Few-shot CIL setting we have improved by at least 14% over the current state-of-the-art methods with significantly less forgetting. | https://openaccess.thecvf.com/content/CVPR2024/papers/Cao_Generative_Multi-modal_Models_are_Good_Class_Incremental_Learners_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.18383 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_Generative_Multi-modal_Models_are_Good_Class_Incremental_Learners_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Cao_Generative_Multi-modal_Models_are_Good_Class_Incremental_Learners_CVPR_2024_paper.html | CVPR 2024 | null | null |
Low-Resource Vision Challenges for Foundation Models | null | null | null | null | null | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Low-Resource_Vision_Challenges_for_Foundation_Models_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Zhang_Low-Resource_Vision_Challenges_for_Foundation_Models_CVPR_2024_paper.html | CVPR 2024 | null | null |
RGBD Objects in the Wild: Scaling Real-World 3D Object Learning from RGB-D Videos | Hongchi Xia, Yang Fu, Sifei Liu, Xiaolong Wang | We introduce a new RGB-D object dataset captured in the wild called WildRGB-D. Unlike most existing real-world object-centric datasets which only come with RGB capturing the direct capture of the depth channel allows better 3D annotations and broader downstream applications. WildRGB-D comprises large-scale category-level RGB-D object videos which are taken using an iPhone to go around the objects in 360 degrees. It contains around 8500 recorded objects and nearly 20000 RGB-D videos across 46 common object categories. These videos are taken with diverse cluttered backgrounds with three setups to cover as many real-world scenarios as possible: (i) a single object in one video; (ii) multiple objects in one video; and (iii) an object with a static hand in one video. The dataset is annotated with object masks real-world scale camera poses and reconstructed aggregated point clouds from RGBD videos. We benchmark four tasks with WildRGB-D including novel view synthesis camera pose estimation object 6d pose estimation and object surface reconstruction. Our experiments show that the large-scale capture of RGB-D objects provides a large potential to advance 3D object learning. Our project page is https://wildrgbd.github.io/. | https://openaccess.thecvf.com/content/CVPR2024/papers/Xia_RGBD_Objects_in_the_Wild_Scaling_Real-World_3D_Object_Learning_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_RGBD_Objects_in_the_Wild_Scaling_Real-World_3D_Object_Learning_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Xia_RGBD_Objects_in_the_Wild_Scaling_Real-World_3D_Object_Learning_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Xia_RGBD_Objects_in_CVPR_2024_supplemental.zip | null |
Shadow-Enlightened Image Outpainting | Hang Yu, Ruilin Li, Shaorong Xie, Jiayan Qiu | Conventional image outpainting methods usually treat unobserved areas as unknown and extend the scene only in terms of semantic consistency thus overlooking the hidden information in shadows cast by unobserved areas such as the invisible shapes and semantics. In this paper we propose to extract and utilize the hidden information of unobserved areas from their shadows to enhance image outpainting. To this end we propose an end-to-end deep approach that explicitly looks into the shadows within the image. Specifically we extract shadows from the input image and identify instance-level shadow regions cast by the unobserved areas. Then the instance-level shadow representations are concatenated to predict the scene layout of each unobserved instance and outpaint the unobserved areas. Finally two discriminators are implemented to enhance alignment between the extended semantics and their shadows. In the experiments we show that our proposed approach provides complementary cues for outpainting and achieves considerable improvement on all datasets by adopting our approach as a plug-in module. | https://openaccess.thecvf.com/content/CVPR2024/papers/Yu_Shadow-Enlightened_Image_Outpainting_CVPR_2024_paper.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Shadow-Enlightened_Image_Outpainting_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Yu_Shadow-Enlightened_Image_Outpainting_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Yu_Shadow-Enlightened_Image_Outpainting_CVPR_2024_supplemental.pdf | null |
Towards Generalizable Tumor Synthesis | Qi Chen, Xiaoxi Chen, Haorui Song, Zhiwei Xiong, Alan Yuille, Chen Wei, Zongwei Zhou | Tumor synthesis enables the creation of artificial tumors in medical images facilitating the training of AI models for tumor detection and segmentation. However success in tumor synthesis hinges on creating visually realistic tumors that are generalizable across multiple organs and furthermore the resulting AI models being capable of detecting real tumors in images sourced from different domains (e.g. hospitals). This paper made a progressive stride toward generalizable tumor synthesis by leveraging a critical observation: early-stage tumors (< 2cm) tend to have similar imaging characteristics in computed tomography (CT) whether they originate in the liver pancreas or kidneys. We have ascertained that generative AI models e.g. Diffusion Models can create realistic tumors generalized to a range of organs even when trained on a limited number of tumor examples from only one organ. Moreover we have shown that AI models trained on these synthetic tumors can be generalized to detect and segment real tumors from CT volumes encompassing a broad spectrum of patient demographics imaging protocols and healthcare facilities. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Towards_Generalizable_Tumor_Synthesis_CVPR_2024_paper.pdf | http://arxiv.org/abs/2402.19470 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Towards_Generalizable_Tumor_Synthesis_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Towards_Generalizable_Tumor_Synthesis_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Towards_Generalizable_Tumor_CVPR_2024_supplemental.pdf | null |
Low-Res Leads the Way: Improving Generalization for Super-Resolution by Self-Supervised Learning | Haoyu Chen, Wenbo Li, Jinjin Gu, Jingjing Ren, Haoze Sun, Xueyi Zou, Zhensong Zhang, Youliang Yan, Lei Zhu | For image super-resolution (SR) bridging the gap between the performance on synthetic datasets and real-world degradation scenarios remains a challenge. This work introduces a novel "Low-Res Leads the Way" (LWay) training framework merging Supervised Pre-training with Self-supervised Learning to enhance the adaptability of SR models to real-world images. Our approach utilizes a low-resolution (LR) reconstruction network to extract degradation embeddings from LR images merging them with super-resolved outputs for LR reconstruction. Leveraging unseen LR images for self-supervised learning guides the model to adapt its modeling space to the target domain facilitating fine-tuning of SR models without requiring paired high-resolution (HR) images. The integration of Discrete Wavelet Transform (DWT) further refines the focus on high-frequency details. Extensive evaluations show that our method significantly improves the generalization and detail restoration capabilities of SR models on unseen real-world datasets outperforming existing methods. Our training regime is universally compatible requiring no network architecture modifications making it a practical solution for real-world SR applications. | https://openaccess.thecvf.com/content/CVPR2024/papers/Chen_Low-Res_Leads_the_Way_Improving_Generalization_for_Super-Resolution_by_Self-Supervised_CVPR_2024_paper.pdf | http://arxiv.org/abs/2403.02601 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Low-Res_Leads_the_Way_Improving_Generalization_for_Super-Resolution_by_Self-Supervised_CVPR_2024_paper.html | https://openaccess.thecvf.com/content/CVPR2024/html/Chen_Low-Res_Leads_the_Way_Improving_Generalization_for_Super-Resolution_by_Self-Supervised_CVPR_2024_paper.html | CVPR 2024 | https://openaccess.thecvf.com/content/CVPR2024/supplemental/Chen_Low-Res_Leads_the_CVPR_2024_supplemental.pdf | null |
Subsets and Splits