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title string | authors string | abstract string | pdf string | supp string | arXiv string | bibtex string | url string | detail_url string | tags string | string |
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Fair Scratch Tickets: Finding Fair Sparse Networks Without Weight Training | Pengwei Tang, Wei Yao, Zhicong Li, Yong Liu | Recent studies suggest that computer vision models come at the risk of compromising fairness. There are extensive works to alleviate unfairness in computer vision using pre-processing, in-processing, and post-processing methods. In this paper, we lead a novel fairness-aware learning paradigm for in-processing methods t... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Fair_Scratch_Tickets_Finding_Fair_Sparse_Networks_Without_Weight_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Fair_Scratch_Tickets_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Fair_Scratch_Tickets_Finding_Fair_Sparse_Networks_Without_Weight_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Fair_Scratch_Tickets_Finding_Fair_Sparse_Networks_Without_Weight_Training_CVPR_2023_paper.html | CVPR 2023 | null |
Generative Bias for Robust Visual Question Answering | Jae Won Cho, Dong-Jin Kim, Hyeonggon Ryu, In So Kweon | The task of Visual Question Answering (VQA) is known to be plagued by the issue of VQA models exploiting biases within the dataset to make its final prediction. Various previous ensemble based debiasing methods have been proposed where an additional model is purposefully trained to be biased in order to train a robust ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_Generative_Bias_for_Robust_Visual_Question_Answering_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2208.00690 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Generative_Bias_for_Robust_Visual_Question_Answering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Generative_Bias_for_Robust_Visual_Question_Answering_CVPR_2023_paper.html | CVPR 2023 | null |
Data-Free Sketch-Based Image Retrieval | Abhra Chaudhuri, Ayan Kumar Bhunia, Yi-Zhe Song, Anjan Dutta | Rising concerns about privacy and anonymity preservation of deep learning models have facilitated research in data-free learning. Primarily based on data-free knowledge distillation, models developed in this area so far have only been able to operate in a single modality, performing the same kind of task as that of the... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chaudhuri_Data-Free_Sketch-Based_Image_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chaudhuri_Data-Free_Sketch-Based_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.07775 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chaudhuri_Data-Free_Sketch-Based_Image_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chaudhuri_Data-Free_Sketch-Based_Image_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Object Manipulation via Object-Centric Neural Scattering Functions | Stephen Tian, Yancheng Cai, Hong-Xing Yu, Sergey Zakharov, Katherine Liu, Adrien Gaidon, Yunzhu Li, Jiajun Wu | Learned visual dynamics models have proven effective for robotic manipulation tasks. Yet, it remains unclear how best to represent scenes involving multi-object interactions. Current methods decompose a scene into discrete objects, yet they struggle with precise modeling and manipulation amid challenging lighting condi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_Multi-Object_Manipulation_via_Object-Centric_Neural_Scattering_Functions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_Multi-Object_Manipulation_via_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Multi-Object_Manipulation_via_Object-Centric_Neural_Scattering_Functions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_Multi-Object_Manipulation_via_Object-Centric_Neural_Scattering_Functions_CVPR_2023_paper.html | CVPR 2023 | null |
The Wisdom of Crowds: Temporal Progressive Attention for Early Action Prediction | Alexandros Stergiou, Dima Damen | Early action prediction deals with inferring the ongoing action from partially-observed videos, typically at the outset of the video. We propose a bottleneck-based attention model that captures the evolution of the action, through progressive sampling over fine-to-coarse scales. Our proposed Temporal Progressive (TemPr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Stergiou_The_Wisdom_of_Crowds_Temporal_Progressive_Attention_for_Early_Action_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Stergiou_The_Wisdom_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.13340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Stergiou_The_Wisdom_of_Crowds_Temporal_Progressive_Attention_for_Early_Action_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Stergiou_The_Wisdom_of_Crowds_Temporal_Progressive_Attention_for_Early_Action_CVPR_2023_paper.html | CVPR 2023 | null |
Invertible Neural Skinning | Yash Kant, Aliaksandr Siarohin, Riza Alp Guler, Menglei Chai, Jian Ren, Sergey Tulyakov, Igor Gilitschenski | Building animatable and editable models of clothed humans from raw 3D scans and poses is a challenging problem. Existing reposing methods suffer from the limited expressiveness of Linear Blend Skinning (LBS), require costly mesh extraction to generate each new pose, and typically do not preserve surface correspondences... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kant_Invertible_Neural_Skinning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kant_Invertible_Neural_Skinning_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.09227 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kant_Invertible_Neural_Skinning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kant_Invertible_Neural_Skinning_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Semantic Segmentation via Adversarial Learning of Classifier and Reconstructor | Hyeokjun Kweon, Sung-Hoon Yoon, Kuk-Jin Yoon | In Weakly Supervised Semantic Segmentation (WSSS), Class Activation Maps (CAMs) usually 1) do not cover the whole object and 2) be activated on irrelevant regions. To address the issues, we propose a novel WSSS framework via adversarial learning of a classifier and an image reconstructor. When an image is perfectly dec... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kweon_Weakly_Supervised_Semantic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kweon_Weakly_Supervised_Semantic_Segmentation_via_Adversarial_Learning_of_Classifier_and_CVPR_2023_paper.html | CVPR 2023 | null |
Intrinsic Physical Concepts Discovery With Object-Centric Predictive Models | Qu Tang, Xiangyu Zhu, Zhen Lei, Zhaoxiang Zhang | The ability to discover abstract physical concepts and understand how they work in the world through observing lies at the core of human intelligence. The acquisition of this ability is based on compositionally perceiving the environment in terms of objects and relations in an unsupervised manner. Recent approaches lea... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Intrinsic_Physical_Concepts_Discovery_With_Object-Centric_Predictive_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tang_Intrinsic_Physical_Concepts_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01869 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Intrinsic_Physical_Concepts_Discovery_With_Object-Centric_Predictive_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Intrinsic_Physical_Concepts_Discovery_With_Object-Centric_Predictive_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Distilling Cross-Temporal Contexts for Continuous Sign Language Recognition | Leming Guo, Wanli Xue, Qing Guo, Bo Liu, Kaihua Zhang, Tiantian Yuan, Shengyong Chen | Continuous sign language recognition (CSLR) aims to recognize glosses in a sign language video. State-of-the-art methods typically have two modules, a spatial perception module and a temporal aggregation module, which are jointly learned end-to-end. Existing results in [9,20,25,36] have indicated that, as the frontal c... | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Guo_Distilling_Cross-Temporal_Contexts_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_Distilling_Cross-Temporal_Contexts_for_Continuous_Sign_Language_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Automatic High Resolution Wire Segmentation and Removal | Mang Tik Chiu, Xuaner Zhang, Zijun Wei, Yuqian Zhou, Eli Shechtman, Connelly Barnes, Zhe Lin, Florian Kainz, Sohrab Amirghodsi, Humphrey Shi | Wires and powerlines are common visual distractions that often undermine the aesthetics of photographs. The manual process of precisely segmenting and removing them is extremely tedious and may take up to hours, especially on high-resolution photos where wires may span the entire space. In this paper, we present an aut... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chiu_Automatic_High_Resolution_Wire_Segmentation_and_Removal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chiu_Automatic_High_Resolution_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00221 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chiu_Automatic_High_Resolution_Wire_Segmentation_and_Removal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chiu_Automatic_High_Resolution_Wire_Segmentation_and_Removal_CVPR_2023_paper.html | CVPR 2023 | null |
The Resource Problem of Using Linear Layer Leakage Attack in Federated Learning | Joshua C. Zhao, Ahmed Roushdy Elkordy, Atul Sharma, Yahya H. Ezzeldin, Salman Avestimehr, Saurabh Bagchi | Secure aggregation promises a heightened level of privacy in federated learning, maintaining that a server only has access to a decrypted aggregate update. Within this setting, linear layer leakage methods are the only data reconstruction attacks able to scale and achieve a high leakage rate regardless of the number of... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_The_Resource_Problem_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14868 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_The_Resource_Problem_of_Using_Linear_Layer_Leakage_Attack_in_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration | Guofeng Mei, Hao Tang, Xiaoshui Huang, Weijie Wang, Juan Liu, Jian Zhang, Luc Van Gool, Qiang Wu | Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data. To address these issues, we propose UDPReg, an unsupervised deep probabilistic registration framework for point clouds with partial overlaps. Specifically, we first adopt a network to learn posterior probability distribu... | https://openaccess.thecvf.com/content/CVPR2023/papers/Mei_Unsupervised_Deep_Probabilistic_Approach_for_Partial_Point_Cloud_Registration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mei_Unsupervised_Deep_Probabilistic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13290 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Unsupervised_Deep_Probabilistic_Approach_for_Partial_Point_Cloud_Registration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mei_Unsupervised_Deep_Probabilistic_Approach_for_Partial_Point_Cloud_Registration_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Generalisable Video Moment Retrieval: Visual-Dynamic Injection to Image-Text Pre-Training | Dezhao Luo, Jiabo Huang, Shaogang Gong, Hailin Jin, Yang Liu | The correlation between the vision and text is essential for video moment retrieval (VMR), however, existing methods heavily rely on separate pre-training feature extractors for visual and textual understanding. Without sufficient temporal boundary annotations, it is non-trivial to learn universal video-text alignments... | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Towards_Generalisable_Video_Moment_Retrieval_Visual-Dynamic_Injection_to_Image-Text_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_Towards_Generalisable_Video_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00040 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Towards_Generalisable_Video_Moment_Retrieval_Visual-Dynamic_Injection_to_Image-Text_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Towards_Generalisable_Video_Moment_Retrieval_Visual-Dynamic_Injection_to_Image-Text_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Adaptive Dense Event Stereo From the Image Domain | Hoonhee Cho, Jegyeong Cho, Kuk-Jin Yoon | Recently, event-based stereo matching has been studied due to its robustness in poor light conditions. However, existing event-based stereo networks suffer severe performance degradation when domains shift. Unsupervised domain adaptation (UDA) aims at resolving this problem without using the target domain ground-truth.... | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_Learning_Adaptive_Dense_Event_Stereo_From_the_Image_Domain_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cho_Learning_Adaptive_Dense_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Learning_Adaptive_Dense_Event_Stereo_From_the_Image_Domain_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_Learning_Adaptive_Dense_Event_Stereo_From_the_Image_Domain_CVPR_2023_paper.html | CVPR 2023 | null |
Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation | Chaohui Yu, Qiang Zhou, Jingliang Li, Jianlong Yuan, Zhibin Wang, Fan Wang | Modern incremental learning for semantic segmentation methods usually learn new categories based on dense annotations. Although achieve promising results, pixel-by-pixel labeling is costly and time-consuming. Weakly incremental learning for semantic segmentation (WILSS) is a novel and attractive task, which aims at lea... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Foundation_Model_Drives_Weakly_Incremental_Learning_for_Semantic_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.14250 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Foundation_Model_Drives_Weakly_Incremental_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Foundation_Model_Drives_Weakly_Incremental_Learning_for_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Seeing a Rose in Five Thousand Ways | Yunzhi Zhang, Shangzhe Wu, Noah Snavely, Jiajun Wu | What is a rose, visually? A rose comprises its intrinsics, including the distribution of geometry, texture, and material specific to its object category. With knowledge of these intrinsic properties, we may render roses of different sizes and shapes, in different poses, and under different lighting conditions. In this ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Seeing_a_Rose_in_Five_Thousand_Ways_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Seeing_a_Rose_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04965 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Seeing_a_Rose_in_Five_Thousand_Ways_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Seeing_a_Rose_in_Five_Thousand_Ways_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Residual Radiance Fields for Streamably Free-Viewpoint Videos | Liao Wang, Qiang Hu, Qihan He, Ziyu Wang, Jingyi Yu, Tinne Tuytelaars, Lan Xu, Minye Wu | The success of the Neural Radiance Fields (NeRFs) for modeling and free-view rendering static objects has inspired numerous attempts on dynamic scenes. Current techniques that utilize neural rendering for facilitating free-view videos (FVVs) are restricted to either offline rendering or are capable of processing only b... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Neural_Residual_Radiance_Fields_for_Streamably_Free-Viewpoint_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Neural_Residual_Radiance_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.04452 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Neural_Residual_Radiance_Fields_for_Streamably_Free-Viewpoint_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Neural_Residual_Radiance_Fields_for_Streamably_Free-Viewpoint_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation | Kehan Li, Zhennan Wang, Zesen Cheng, Runyi Yu, Yian Zhao, Guoli Song, Chang Liu, Li Yuan, Jie Chen | Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction tasks, e.g., unsupervised semantic segmentation (USS). The extracted relationship among pixel-level repre... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_ACSeg_Adaptive_Conceptualization_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_ACSeg_Adaptive_Conceptualization_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2210.05944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ACSeg_Adaptive_Conceptualization_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ACSeg_Adaptive_Conceptualization_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
NeRFVS: Neural Radiance Fields for Free View Synthesis via Geometry Scaffolds | Chen Yang, Peihao Li, Zanwei Zhou, Shanxin Yuan, Bingbing Liu, Xiaokang Yang, Weichao Qiu, Wei Shen | We present NeRFVS, a novel neural radiance fields (NeRF) based method to enable free navigation in a room. NeRF achieves impressive performance in rendering images for novel views similar to the input views while suffering for novel views that are significantly different from the training views. To address this issue, ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_NeRFVS_Neural_Radiance_Fields_for_Free_View_Synthesis_via_Geometry_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_NeRFVS_Neural_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06287 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_NeRFVS_Neural_Radiance_Fields_for_Free_View_Synthesis_via_Geometry_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_NeRFVS_Neural_Radiance_Fields_for_Free_View_Synthesis_via_Geometry_CVPR_2023_paper.html | CVPR 2023 | null |
Reproducible Scaling Laws for Contrastive Language-Image Learning | Mehdi Cherti, Romain Beaumont, Ross Wightman, Mitchell Wortsman, Gabriel Ilharco, Cade Gordon, Christoph Schuhmann, Ludwig Schmidt, Jenia Jitsev | Scaling up neural networks has led to remarkable performance across a wide range of tasks. Moreover, performance often follows reliable scaling laws as a function of training set size, model size, and compute, which offers valuable guidance as large-scale experiments are becoming increasingly expensive. However, previo... | https://openaccess.thecvf.com/content/CVPR2023/papers/Cherti_Reproducible_Scaling_Laws_for_Contrastive_Language-Image_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cherti_Reproducible_Scaling_Laws_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07143 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cherti_Reproducible_Scaling_Laws_for_Contrastive_Language-Image_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cherti_Reproducible_Scaling_Laws_for_Contrastive_Language-Image_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Similarity Metric Learning for RGB-Infrared Group Re-Identification | Jianghao Xiong, Jianhuang Lai | Group re-identification (G-ReID) aims to re-identify a group of people that is observed from non-overlapping camera systems. The existing literature has mainly addressed RGB-based problems, but RGB-infrared (RGB-IR) cross-modality matching problem has not been studied yet. In this paper, we propose a metric learning me... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiong_Similarity_Metric_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiong_Similarity_Metric_Learning_for_RGB-Infrared_Group_Re-Identification_CVPR_2023_paper.html | CVPR 2023 | null |
Auto-CARD: Efficient and Robust Codec Avatar Driving for Real-Time Mobile Telepresence | Yonggan Fu, Yuecheng Li, Chenghui Li, Jason Saragih, Peizhao Zhang, Xiaoliang Dai, Yingyan (Celine) Lin | Real-time and robust photorealistic avatars for telepresence in AR/VR have been highly desired for enabling immersive photorealistic telepresence. However, there still exists one key bottleneck: the considerable computational expense needed to accurately infer facial expressions captured from headset-mounted cameras wi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fu_Auto-CARD_Efficient_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Auto-CARD_Efficient_and_Robust_Codec_Avatar_Driving_for_Real-Time_Mobile_CVPR_2023_paper.html | CVPR 2023 | null |
Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching | Paul Roetzer, Zorah Lähner, Florian Bernard | We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh. While such problems can be solved to global optimality by finding a shortest path in the product graph between both shapes, existing solutions heavily rely on unrealistic prior assumptions to avoid degenerate solu... | https://openaccess.thecvf.com/content/CVPR2023/papers/Roetzer_Conjugate_Product_Graphs_for_Globally_Optimal_2D-3D_Shape_Matching_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Roetzer_Conjugate_Product_Graphs_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Roetzer_Conjugate_Product_Graphs_for_Globally_Optimal_2D-3D_Shape_Matching_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Roetzer_Conjugate_Product_Graphs_for_Globally_Optimal_2D-3D_Shape_Matching_CVPR_2023_paper.html | CVPR 2023 | null |
PromptCAL: Contrastive Affinity Learning via Auxiliary Prompts for Generalized Novel Category Discovery | Sheng Zhang, Salman Khan, Zhiqiang Shen, Muzammal Naseer, Guangyi Chen, Fahad Shahbaz Khan | Although existing semi-supervised learning models achieve remarkable success in learning with unannotated in-distribution data, they mostly fail to learn on unlabeled data sampled from novel semantic classes due to their closed-set assumption. In this work, we target a pragmatic but under-explored Generalized Novel Cat... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_PromptCAL_Contrastive_Affinity_Learning_via_Auxiliary_Prompts_for_Generalized_Novel_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_PromptCAL_Contrastive_Affinity_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05590 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PromptCAL_Contrastive_Affinity_Learning_via_Auxiliary_Prompts_for_Generalized_Novel_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_PromptCAL_Contrastive_Affinity_Learning_via_Auxiliary_Prompts_for_Generalized_Novel_CVPR_2023_paper.html | CVPR 2023 | null |
Train/Test-Time Adaptation With Retrieval | Luca Zancato, Alessandro Achille, Tian Yu Liu, Matthew Trager, Pramuditha Perera, Stefano Soatto | We introduce Train/Test-Time Adaptation with Retrieval (T3AR), a method to adapt models both at train and test time by means of a retrieval module and a searchable pool of external samples. Before inference, T3AR adapts a given model to the downstream task using refined pseudo-labels and a self-supervised contrastive o... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zancato_TrainTest-Time_Adaptation_With_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zancato_TrainTest-Time_Adaptation_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14333 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zancato_TrainTest-Time_Adaptation_With_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zancato_TrainTest-Time_Adaptation_With_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
ProxyFormer: Proxy Alignment Assisted Point Cloud Completion With Missing Part Sensitive Transformer | Shanshan Li, Pan Gao, Xiaoyang Tan, Mingqiang Wei | Problems such as equipment defects or limited viewpoints will lead the captured point clouds to be incomplete. Therefore, recovering the complete point clouds from the partial ones plays an vital role in many practical tasks, and one of the keys lies in the prediction of the missing part. In this paper, we propose a no... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_ProxyFormer_Proxy_Alignment_Assisted_Point_Cloud_Completion_With_Missing_Part_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_ProxyFormer_Proxy_Alignment_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14435 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ProxyFormer_Proxy_Alignment_Assisted_Point_Cloud_Completion_With_Missing_Part_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ProxyFormer_Proxy_Alignment_Assisted_Point_Cloud_Completion_With_Missing_Part_CVPR_2023_paper.html | CVPR 2023 | null |
Mod-Squad: Designing Mixtures of Experts As Modular Multi-Task Learners | Zitian Chen, Yikang Shen, Mingyu Ding, Zhenfang Chen, Hengshuang Zhao, Erik G. Learned-Miller, Chuang Gan | Optimization in multi-task learning (MTL) is more challenging than single-task learning (STL), as the gradient from different tasks can be contradictory. When tasks are related, it can be beneficial to share some parameters among them (cooperation). However, some tasks require additional parameters with expertise in a ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Mod-Squad_Designing_Mixtures_of_Experts_As_Modular_Multi-Task_Learners_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Mod-Squad_Designing_Mixtures_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Mod-Squad_Designing_Mixtures_of_Experts_As_Modular_Multi-Task_Learners_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Mod-Squad_Designing_Mixtures_of_Experts_As_Modular_Multi-Task_Learners_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Customized Visual Models With Retrieval-Augmented Knowledge | Haotian Liu, Kilho Son, Jianwei Yang, Ce Liu, Jianfeng Gao, Yong Jae Lee, Chunyuan Li | Image-text contrastive learning models such as CLIP have demonstrated strong task transfer ability. The high generality and usability of these visual models is achieved via a web-scale data collection process to ensure broad concept coverage, followed by expensive pre-training to feed all the knowledge into model weigh... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Learning_Customized_Visual_Models_With_Retrieval-Augmented_Knowledge_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Learning_Customized_Visual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07094 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Learning_Customized_Visual_Models_With_Retrieval-Augmented_Knowledge_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Learning_Customized_Visual_Models_With_Retrieval-Augmented_Knowledge_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Realism Image Compression With a Conditional Generator | Eirikur Agustsson, David Minnen, George Toderici, Fabian Mentzer | By optimizing the rate-distortion-realism trade-off, generative compression approaches produce detailed, realistic images, even at low bit rates, instead of the blurry reconstructions produced by rate-distortion optimized models. However, previous methods do not explicitly control how much detail is synthesized, which ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Agustsson_Multi-Realism_Image_Compression_With_a_Conditional_Generator_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Agustsson_Multi-Realism_Image_Compression_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.13824 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Agustsson_Multi-Realism_Image_Compression_With_a_Conditional_Generator_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Agustsson_Multi-Realism_Image_Compression_With_a_Conditional_Generator_CVPR_2023_paper.html | CVPR 2023 | null |
Run, Don't Walk: Chasing Higher FLOPS for Faster Neural Networks | Jierun Chen, Shiu-hong Kao, Hao He, Weipeng Zhuo, Song Wen, Chul-Ho Lee, S.-H. Gary Chan | To design fast neural networks, many works have been focusing on reducing the number of floating-point operations (FLOPs). We observe that such reduction in FLOPs, however, does not necessarily lead to a similar level of reduction in latency. This mainly stems from inefficiently low floating-point operations per second... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Run_Dont_Walk_Chasing_Higher_FLOPS_for_Faster_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Run_Dont_Walk_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Run_Dont_Walk_Chasing_Higher_FLOPS_for_Faster_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Run_Dont_Walk_Chasing_Higher_FLOPS_for_Faster_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
A Unified Spatial-Angular Structured Light for Single-View Acquisition of Shape and Reflectance | Xianmin Xu, Yuxin Lin, Haoyang Zhou, Chong Zeng, Yaxin Yu, Kun Zhou, Hongzhi Wu | We propose a unified structured light, consisting of an LED array and an LCD mask, for high-quality acquisition of both shape and reflectance from a single view. For geometry, one LED projects a set of learned mask patterns to accurately encode spatial information; the decoded results from multiple LEDs are then aggreg... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_A_Unified_Spatial-Angular_Structured_Light_for_Single-View_Acquisition_of_Shape_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_A_Unified_Spatial-Angular_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_A_Unified_Spatial-Angular_Structured_Light_for_Single-View_Acquisition_of_Shape_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_A_Unified_Spatial-Angular_Structured_Light_for_Single-View_Acquisition_of_Shape_CVPR_2023_paper.html | CVPR 2023 | null |
Best of Both Worlds: Multimodal Contrastive Learning With Tabular and Imaging Data | Paul Hager, Martin J. Menten, Daniel Rueckert | Medical datasets and especially biobanks, often contain extensive tabular data with rich clinical information in addition to images. In practice, clinicians typically have less data, both in terms of diversity and scale, but still wish to deploy deep learning solutions. Combined with increasing medical dataset sizes an... | https://openaccess.thecvf.com/content/CVPR2023/papers/Hager_Best_of_Both_Worlds_Multimodal_Contrastive_Learning_With_Tabular_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hager_Best_of_Both_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14080 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hager_Best_of_Both_Worlds_Multimodal_Contrastive_Learning_With_Tabular_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hager_Best_of_Both_Worlds_Multimodal_Contrastive_Learning_With_Tabular_and_CVPR_2023_paper.html | CVPR 2023 | null |
On the Difficulty of Unpaired Infrared-to-Visible Video Translation: Fine-Grained Content-Rich Patches Transfer | Zhenjie Yu, Shuang Li, Yirui Shen, Chi Harold Liu, Shuigen Wang | Explicit visible videos can provide sufficient visual information and facilitate vision applications. Unfortunately, the image sensors of visible cameras are sensitive to light conditions like darkness or overexposure. To make up for this, recently, infrared sensors capable of stable imaging have received increasing at... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_On_the_Difficulty_of_Unpaired_Infrared-to-Visible_Video_Translation_Fine-Grained_Content-Rich_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_On_the_Difficulty_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_On_the_Difficulty_of_Unpaired_Infrared-to-Visible_Video_Translation_Fine-Grained_Content-Rich_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_On_the_Difficulty_of_Unpaired_Infrared-to-Visible_Video_Translation_Fine-Grained_Content-Rich_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Images Are Counterfactual Samples for Robust Fine-Tuning | Yao Xiao, Ziyi Tang, Pengxu Wei, Cong Liu, Liang Lin | Deep learning models are challenged by the distribution shift between the training data and test data. Recently, the large models pre-trained on diverse data have demonstrated unprecedented robustness to various distribution shifts. However, fine-tuning these models can lead to a trade-off between in-distribution (ID) ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xiao_Masked_Images_Are_Counterfactual_Samples_for_Robust_Fine-Tuning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xiao_Masked_Images_Are_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03052 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_Masked_Images_Are_Counterfactual_Samples_for_Robust_Fine-Tuning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xiao_Masked_Images_Are_Counterfactual_Samples_for_Robust_Fine-Tuning_CVPR_2023_paper.html | CVPR 2023 | null |
StepFormer: Self-Supervised Step Discovery and Localization in Instructional Videos | Nikita Dvornik, Isma Hadji, Ran Zhang, Konstantinos G. Derpanis, Richard P. Wildes, Allan D. Jepson | Instructional videos are an important resource to learn procedural tasks from human demonstrations. However, the instruction steps in such videos are typically short and sparse, with most of the video being irrelevant to the procedure. This motivates the need to temporally localize the instruction steps in such videos,... | https://openaccess.thecvf.com/content/CVPR2023/papers/Dvornik_StepFormer_Self-Supervised_Step_Discovery_and_Localization_in_Instructional_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dvornik_StepFormer_Self-Supervised_Step_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.13265 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dvornik_StepFormer_Self-Supervised_Step_Discovery_and_Localization_in_Instructional_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dvornik_StepFormer_Self-Supervised_Step_Discovery_and_Localization_in_Instructional_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Procedure-Aware Video Representation From Instructional Videos and Their Narrations | Yiwu Zhong, Licheng Yu, Yang Bai, Shangwen Li, Xueting Yan, Yin Li | The abundance of instructional videos and their narrations over the Internet offers an exciting avenue for understanding procedural activities. In this work, we propose to learn video representation that encodes both action steps and their temporal ordering, based on a large-scale dataset of web instructional videos an... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Learning_Procedure-Aware_Video_Representation_From_Instructional_Videos_and_Their_Narrations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Learning_Procedure-Aware_Video_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17839 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Learning_Procedure-Aware_Video_Representation_From_Instructional_Videos_and_Their_Narrations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Learning_Procedure-Aware_Video_Representation_From_Instructional_Videos_and_Their_Narrations_CVPR_2023_paper.html | CVPR 2023 | null |
Open Vocabulary Semantic Segmentation With Patch Aligned Contrastive Learning | Jishnu Mukhoti, Tsung-Yu Lin, Omid Poursaeed, Rui Wang, Ashish Shah, Philip H.S. Torr, Ser-Nam Lim | We introduce Patch Aligned Contrastive Learning (PACL), a modified compatibility function for CLIP's contrastive loss, intending to train an alignment between the patch tokens of the vision encoder and the CLS token of the text encoder. With such an alignment, a model can identify regions of an image corresponding to a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Mukhoti_Open_Vocabulary_Semantic_Segmentation_With_Patch_Aligned_Contrastive_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mukhoti_Open_Vocabulary_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04994 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mukhoti_Open_Vocabulary_Semantic_Segmentation_With_Patch_Aligned_Contrastive_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mukhoti_Open_Vocabulary_Semantic_Segmentation_With_Patch_Aligned_Contrastive_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP the Gap: A Single Domain Generalization Approach for Object Detection | Vidit Vidit, Martin Engilberge, Mathieu Salzmann | Single Domain Generalization (SDG) tackles the problem of training a model on a single source domain so that it generalizes to any unseen target domain. While this has been well studied for image classification, the literature on SDG object detection remains almost non-existent. To address the challenges of simultaneou... | https://openaccess.thecvf.com/content/CVPR2023/papers/Vidit_CLIP_the_Gap_A_Single_Domain_Generalization_Approach_for_Object_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vidit_CLIP_the_Gap_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05499 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vidit_CLIP_the_Gap_A_Single_Domain_Generalization_Approach_for_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vidit_CLIP_the_Gap_A_Single_Domain_Generalization_Approach_for_Object_CVPR_2023_paper.html | CVPR 2023 | null |
Co-Training 2L Submodels for Visual Recognition | Hugo Touvron, Matthieu Cord, Maxime Oquab, Piotr Bojanowski, Jakob Verbeek, Hervé Jégou | This paper introduces submodel co-training, a regularization method related to co-training, self-distillation and stochastic depth. Given a neural network to be trained, for each sample we implicitly instantiate two altered networks, "submodels", with stochastic depth: i.e. activating only a subset of the layers and sk... | https://openaccess.thecvf.com/content/CVPR2023/papers/Touvron_Co-Training_2L_Submodels_for_Visual_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Touvron_Co-Training_2L_Submodels_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Touvron_Co-Training_2L_Submodels_for_Visual_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Touvron_Co-Training_2L_Submodels_for_Visual_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
On the Importance of Accurate Geometry Data for Dense 3D Vision Tasks | HyunJun Jung, Patrick Ruhkamp, Guangyao Zhai, Nikolas Brasch, Yitong Li, Yannick Verdie, Jifei Song, Yiren Zhou, Anil Armagan, Slobodan Ilic, Aleš Leonardis, Nassir Navab, Benjamin Busam | Learning-based methods to solve dense 3D vision problems typically train on 3D sensor data. The respectively used principle of measuring distances provides advantages and drawbacks. These are typically not compared nor discussed in the literature due to a lack of multi-modal datasets. Texture-less regions are problemat... | https://openaccess.thecvf.com/content/CVPR2023/papers/Jung_On_the_Importance_of_Accurate_Geometry_Data_for_Dense_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jung_On_the_Importance_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.14840 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jung_On_the_Importance_of_Accurate_Geometry_Data_for_Dense_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jung_On_the_Importance_of_Accurate_Geometry_Data_for_Dense_3D_CVPR_2023_paper.html | CVPR 2023 | null |
Camouflaged Instance Segmentation via Explicit De-Camouflaging | Naisong Luo, Yuwen Pan, Rui Sun, Tianzhu Zhang, Zhiwei Xiong, Feng Wu | Camouflaged Instance Segmentation (CIS) aims at predicting the instance-level masks of camouflaged objects, which are usually the animals in the wild adapting their appearance to match the surroundings. Previous instance segmentation methods perform poorly on this task as they are easily disturbed by the deceptive camo... | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Camouflaged_Instance_Segmentation_via_Explicit_De-Camouflaging_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_Camouflaged_Instance_Segmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Camouflaged_Instance_Segmentation_via_Explicit_De-Camouflaging_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Camouflaged_Instance_Segmentation_via_Explicit_De-Camouflaging_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding Masked Autoencoders via Hierarchical Latent Variable Models | Lingjing Kong, Martin Q. Ma, Guangyi Chen, Eric P. Xing, Yuejie Chi, Louis-Philippe Morency, Kun Zhang | Masked autoencoder (MAE), a simple and effective self-supervised learning framework based on the reconstruction of masked image regions, has recently achieved prominent success in a variety of vision tasks. Despite the emergence of intriguing empirical observations on MAE, a theoretically principled understanding is st... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kong_Understanding_Masked_Autoencoders_via_Hierarchical_Latent_Variable_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kong_Understanding_Masked_Autoencoders_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Understanding_Masked_Autoencoders_via_Hierarchical_Latent_Variable_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kong_Understanding_Masked_Autoencoders_via_Hierarchical_Latent_Variable_Models_CVPR_2023_paper.html | CVPR 2023 | null |
K-Planes: Explicit Radiance Fields in Space, Time, and Appearance | Sara Fridovich-Keil, Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, Angjoo Kanazawa | We introduce k-planes, a white-box model for radiance fields in arbitrary dimensions. Our model uses d-choose-2 planes to represent a d-dimensional scene, providing a seamless way to go from static (d=3) to dynamic (d=4) scenes. This planar factorization makes adding dimension-specific priors easy, e.g. temporal smooth... | https://openaccess.thecvf.com/content/CVPR2023/papers/Fridovich-Keil_K-Planes_Explicit_Radiance_Fields_in_Space_Time_and_Appearance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Fridovich-Keil_K-Planes_Explicit_Radiance_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fridovich-Keil_K-Planes_Explicit_Radiance_Fields_in_Space_Time_and_Appearance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fridovich-Keil_K-Planes_Explicit_Radiance_Fields_in_Space_Time_and_Appearance_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Mode Online Knowledge Distillation for Self-Supervised Visual Representation Learning | Kaiyou Song, Jin Xie, Shan Zhang, Zimeng Luo | Self-supervised learning (SSL) has made remarkable progress in visual representation learning. Some studies combine SSL with knowledge distillation (SSL-KD) to boost the representation learning performance of small models. In this study, we propose a Multi-mode Online Knowledge Distillation method (MOKD) to boost self-... | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Multi-Mode_Online_Knowledge_Distillation_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_Multi-Mode_Online_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06461 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Multi-Mode_Online_Knowledge_Distillation_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Multi-Mode_Online_Knowledge_Distillation_for_Self-Supervised_Visual_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Unbalanced Optimal Transport: A Unified Framework for Object Detection | Henri De Plaen, Pierre-François De Plaen, Johan A. K. Suykens, Marc Proesmans, Tinne Tuytelaars, Luc Van Gool | During training, supervised object detection tries to correctly match the predicted bounding boxes and associated classification scores to the ground truth. This is essential to determine which predictions are to be pushed towards which solutions, or to be discarded. Popular matching strategies include matching to the ... | https://openaccess.thecvf.com/content/CVPR2023/papers/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/De_Plaen_Unbalanced_Optimal_Transport_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/De_Plaen_Unbalanced_Optimal_Transport_A_Unified_Framework_for_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Viewpoint Equivariance for Multi-View 3D Object Detection | Dian Chen, Jie Li, Vitor Guizilini, Rares Andrei Ambrus, Adrien Gaidon | 3D object detection from visual sensors is a cornerstone capability of robotic systems. State-of-the-art methods focus on reasoning and decoding object bounding boxes from multi-view camera input. In this work we gain intuition from the integral role of multi-view consistency in 3D scene understanding and geometric lea... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Viewpoint_Equivariance_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Viewpoint_Equivariance_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14548 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Viewpoint_Equivariance_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Viewpoint_Equivariance_for_Multi-View_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Photo Pre-Training, but for Sketch | Ke Li, Kaiyue Pang, Yi-Zhe Song | The sketch community has faced up to its unique challenges over the years, that of data scarcity however still remains the most significant to date. This lack of sketch data has imposed on the community a few "peculiar" design choices -- the most representative of them all is perhaps the coerced utilisation of photo-ba... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Photo_Pre-Training_but_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Photo_Pre-Training_but_for_Sketch_CVPR_2023_paper.html | CVPR 2023 | null |
NeuralPCI: Spatio-Temporal Neural Field for 3D Point Cloud Multi-Frame Non-Linear Interpolation | Zehan Zheng, Danni Wu, Ruisi Lu, Fan Lu, Guang Chen, Changjun Jiang | In recent years, there has been a significant increase in focus on the interpolation task of computer vision. Despite the tremendous advancement of video interpolation, point cloud interpolation remains insufficiently explored. Meanwhile, the existence of numerous nonlinear large motions in real-world scenarios makes t... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_NeuralPCI_Spatio-Temporal_Neural_Field_for_3D_Point_Cloud_Multi-Frame_Non-Linear_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_NeuralPCI_Spatio-Temporal_Neural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_NeuralPCI_Spatio-Temporal_Neural_Field_for_3D_Point_Cloud_Multi-Frame_Non-Linear_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_NeuralPCI_Spatio-Temporal_Neural_Field_for_3D_Point_Cloud_Multi-Frame_Non-Linear_CVPR_2023_paper.html | CVPR 2023 | null |
Bidirectional Cross-Modal Knowledge Exploration for Video Recognition With Pre-Trained Vision-Language Models | Wenhao Wu, Xiaohan Wang, Haipeng Luo, Jingdong Wang, Yi Yang, Wanli Ouyang | Vision-language models (VLMs) pre-trained on large-scale image-text pairs have demonstrated impressive transferability on various visual tasks. Transferring knowledge from such powerful VLMs is a promising direction for building effective video recognition models. However, current exploration in this field is still lim... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Bidirectional_Cross-Modal_Knowledge_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.00182 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Bidirectional_Cross-Modal_Knowledge_Exploration_for_Video_Recognition_With_Pre-Trained_Vision-Language_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Plasticity Improvement for Continual Learning | Yan-Shuo Liang, Wu-Jun Li | Many works have tried to solve the catastrophic forgetting (CF) problem in continual learning (lifelong learning). However, pursuing non-forgetting on old tasks may damage the model's plasticity for new tasks. Although some methods have been proposed to achieve stability-plasticity trade-off, no methods have considered... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liang_Adaptive_Plasticity_Improvement_for_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liang_Adaptive_Plasticity_Improvement_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Adaptive_Plasticity_Improvement_for_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liang_Adaptive_Plasticity_Improvement_for_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Pic2Word: Mapping Pictures to Words for Zero-Shot Composed Image Retrieval | Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, Tomas Pfister | In Composed Image Retrieval (CIR), a user combines a query image with text to describe their intended target. Existing methods rely on supervised learning of CIR models using labeled triplets consisting of the query image, text specification, and the target image. Labeling such triplets is expensive and hinders broad a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Saito_Pic2Word_Mapping_Pictures_to_Words_for_Zero-Shot_Composed_Image_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Saito_Pic2Word_Mapping_Pictures_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.03084 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Saito_Pic2Word_Mapping_Pictures_to_Words_for_Zero-Shot_Composed_Image_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Saito_Pic2Word_Mapping_Pictures_to_Words_for_Zero-Shot_Composed_Image_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
MMANet: Margin-Aware Distillation and Modality-Aware Regularization for Incomplete Multimodal Learning | Shicai Wei, Chunbo Luo, Yang Luo | Multimodal learning has shown great potentials in numerous scenes and attracts increasing interest recently. However, it often encounters the problem of missing modality data and thus suffers severe performance degradation in practice. To this end, we propose a general framework called MMANet to assist incomplete multi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_MMANet_Margin-Aware_Distillation_and_Modality-Aware_Regularization_for_Incomplete_Multimodal_Learning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.08028 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_MMANet_Margin-Aware_Distillation_and_Modality-Aware_Regularization_for_Incomplete_Multimodal_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_MMANet_Margin-Aware_Distillation_and_Modality-Aware_Regularization_for_Incomplete_Multimodal_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Putting People in Their Place: Affordance-Aware Human Insertion Into Scenes | Sumith Kulal, Tim Brooks, Alex Aiken, Jiajun Wu, Jimei Yang, Jingwan Lu, Alexei A. Efros, Krishna Kumar Singh | We study the problem of inferring scene affordances by presenting a method for realistically inserting people into scenes. Given a scene image with a marked region and an image of a person, we insert the person into the scene while respecting the scene affordances. Our model can infer the set of realistic poses given t... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kulal_Putting_People_in_Their_Place_Affordance-Aware_Human_Insertion_Into_Scenes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kulal_Putting_People_in_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14406 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kulal_Putting_People_in_Their_Place_Affordance-Aware_Human_Insertion_Into_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kulal_Putting_People_in_Their_Place_Affordance-Aware_Human_Insertion_Into_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
3D Neural Field Generation Using Triplane Diffusion | J. Ryan Shue, Eric Ryan Chan, Ryan Po, Zachary Ankner, Jiajun Wu, Gordon Wetzstein | Diffusion models have emerged as the state-of-the-art for image generation, among other tasks. Here, we present an efficient diffusion-based model for 3D-aware generation of neural fields. Our approach pre-processes training data, such as ShapeNet meshes, by converting them to continuous occupancy fields and factoring ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Shue_3D_Neural_Field_Generation_Using_Triplane_Diffusion_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shue_3D_Neural_Field_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16677 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shue_3D_Neural_Field_Generation_Using_Triplane_Diffusion_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shue_3D_Neural_Field_Generation_Using_Triplane_Diffusion_CVPR_2023_paper.html | CVPR 2023 | null |
Regularized Vector Quantization for Tokenized Image Synthesis | Jiahui Zhang, Fangneng Zhan, Christian Theobalt, Shijian Lu | Quantizing images into discrete representations has been a fundamental problem in unified generative modeling. Predominant approaches learn the discrete representation either in a deterministic manner by selecting the best-matching token or in a stochastic manner by sampling from a predicted distribution. However, dete... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Regularized_Vector_Quantization_for_Tokenized_Image_Synthesis_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.06424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Regularized_Vector_Quantization_for_Tokenized_Image_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Regularized_Vector_Quantization_for_Tokenized_Image_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
Semantic Scene Completion With Cleaner Self | Fengyun Wang, Dong Zhang, Hanwang Zhang, Jinhui Tang, Qianru Sun | Semantic Scene Completion (SSC) transforms an image of single-view depth and/or RGB 2D pixels into 3D voxels, each of whose semantic labels are predicted. SSC is a well-known ill-posed problem as the prediction model has to "imagine" what is behind the visible surface, which is usually represented by Truncated Signed D... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Semantic_Scene_Completion_With_Cleaner_Self_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Semantic_Scene_Completion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09977 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Semantic_Scene_Completion_With_Cleaner_Self_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Semantic_Scene_Completion_With_Cleaner_Self_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Image Recognition by Retrieving From Web-Scale Image-Text Data | Ahmet Iscen, Alireza Fathi, Cordelia Schmid | Retrieval augmented models are becoming increasingly popular for computer vision tasks after their recent success in NLP problems. The goal is to enhance the recognition capabilities of the model by retrieving similar examples for the visual input from an external memory set. In this work, we introduce an attention-bas... | https://openaccess.thecvf.com/content/CVPR2023/papers/Iscen_Improving_Image_Recognition_by_Retrieving_From_Web-Scale_Image-Text_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Iscen_Improving_Image_Recognition_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05173 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Iscen_Improving_Image_Recognition_by_Retrieving_From_Web-Scale_Image-Text_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Iscen_Improving_Image_Recognition_by_Retrieving_From_Web-Scale_Image-Text_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Factorized Metric Learning | Chengkun Wang, Wenzhao Zheng, Junlong Li, Jie Zhou, Jiwen Lu | Learning a generalizable and comprehensive similarity metric to depict the semantic discrepancies between images is the foundation of many computer vision tasks. While existing methods approach this goal by learning an ensemble of embeddings with diverse objectives, the backbone network still receives a mix of all the ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Deep_Factorized_Metric_Learning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Factorized_Metric_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Deep_Factorized_Metric_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity 3D Face Generation From Natural Language Descriptions | Menghua Wu, Hao Zhu, Linjia Huang, Yiyu Zhuang, Yuanxun Lu, Xun Cao | Synthesizing high-quality 3D face models from natural language descriptions is very valuable for many applications, including avatar creation, virtual reality, and telepresence. However, little research ever tapped into this task. We argue the major obstacle lies in 1) the lack of high-quality 3D face data with descrip... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_High-Fidelity_3D_Face_Generation_From_Natural_Language_Descriptions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_High-Fidelity_3D_Face_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.03302 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_High-Fidelity_3D_Face_Generation_From_Natural_Language_Descriptions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_High-Fidelity_3D_Face_Generation_From_Natural_Language_Descriptions_CVPR_2023_paper.html | CVPR 2023 | null |
A Generalized Framework for Video Instance Segmentation | Miran Heo, Sukjun Hwang, Jeongseok Hyun, Hanjung Kim, Seoung Wug Oh, Joon-Young Lee, Seon Joo Kim | The handling of long videos with complex and occluded sequences has recently emerged as a new challenge in the video instance segmentation (VIS) community. However, existing methods have limitations in addressing this challenge. We argue that the biggest bottleneck in current approaches is the discrepancy between train... | https://openaccess.thecvf.com/content/CVPR2023/papers/Heo_A_Generalized_Framework_for_Video_Instance_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Heo_A_Generalized_Framework_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.08834 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Heo_A_Generalized_Framework_for_Video_Instance_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Heo_A_Generalized_Framework_for_Video_Instance_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Level Logit Distillation | Ying Jin, Jiaqi Wang, Dahua Lin | Knowledge Distillation (KD) aims at distilling the knowledge from the large teacher model to a lightweight student model. Mainstream KD methods can be divided into two categories, logit distillation, and feature distillation. The former is easy to implement, but inferior in performance, while the latter is not applicab... | https://openaccess.thecvf.com/content/CVPR2023/papers/Jin_Multi-Level_Logit_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jin_Multi-Level_Logit_Distillation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Multi-Level_Logit_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jin_Multi-Level_Logit_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
On Distillation of Guided Diffusion Models | Chenlin Meng, Robin Rombach, Ruiqi Gao, Diederik Kingma, Stefano Ermon, Jonathan Ho, Tim Salimans | Classifier-free guided diffusion models have recently been shown to be highly effective at high-resolution image generation, and they have been widely used in large-scale diffusion frameworks including DALL*E 2, Stable Diffusion and Imagen. However, a downside of classifier-free guided diffusion models is that they are... | https://openaccess.thecvf.com/content/CVPR2023/papers/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Meng_On_Distillation_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2210.03142 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Meng_On_Distillation_of_Guided_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Dual-Path Adaptation From Image to Video Transformers | Jungin Park, Jiyoung Lee, Kwanghoon Sohn | In this paper, we efficiently transfer the surpassing representation power of the vision foundation models, such as ViT and Swin, for video understanding with only a few trainable parameters. Previous adaptation methods have simultaneously considered spatial and temporal modeling with a unified learnable module but sti... | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Dual-Path_Adaptation_From_Image_to_Video_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Dual-Path_Adaptation_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09857 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Dual-Path_Adaptation_From_Image_to_Video_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Dual-Path_Adaptation_From_Image_to_Video_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Better Decision Forests: Forest Alternating Optimization | Miguel Á. Carreira-Perpiñán, Magzhan Gabidolla, Arman Zharmagambetov | Decision forests are among the most accurate models in machine learning. This is remarkable given that the way they are trained is highly heuristic: neither the individual trees nor the overall forest optimize any well-defined loss. While diversity mechanisms such as bagging or boosting have been until now critical in ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Carreira-Perpinan_Towards_Better_Decision_Forests_Forest_Alternating_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Carreira-Perpinan_Towards_Better_Decision_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Carreira-Perpinan_Towards_Better_Decision_Forests_Forest_Alternating_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Carreira-Perpinan_Towards_Better_Decision_Forests_Forest_Alternating_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
DA Wand: Distortion-Aware Selection Using Neural Mesh Parameterization | Richard Liu, Noam Aigerman, Vladimir G. Kim, Rana Hanocka | We present a neural technique for learning to select a local sub-region around a point which can be used for mesh parameterization. The motivation for our framework is driven by interactive workflows used for decaling, texturing, or painting on surfaces. Our key idea to to learn a local parameterization in a data-drive... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_DA_Wand_Distortion-Aware_Selection_Using_Neural_Mesh_Parameterization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_DA_Wand_Distortion-Aware_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.06344 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_DA_Wand_Distortion-Aware_Selection_Using_Neural_Mesh_Parameterization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_DA_Wand_Distortion-Aware_Selection_Using_Neural_Mesh_Parameterization_CVPR_2023_paper.html | CVPR 2023 | null |
Disentangled Representation Learning for Unsupervised Neural Quantization | Haechan Noh, Sangeek Hyun, Woojin Jeong, Hanshin Lim, Jae-Pil Heo | The inverted index is a widely used data structure to avoid the infeasible exhaustive search. It accelerates retrieval significantly by splitting the database into multiple disjoint sets and restricts distance computation to a small fraction of the database. Moreover, it even improves search quality by allowing quantiz... | https://openaccess.thecvf.com/content/CVPR2023/papers/Noh_Disentangled_Representation_Learning_for_Unsupervised_Neural_Quantization_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Noh_Disentangled_Representation_Learning_for_Unsupervised_Neural_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Noh_Disentangled_Representation_Learning_for_Unsupervised_Neural_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Semantic Correspondence Networks for Video Paragraph Grounding | Chaolei Tan, Zihang Lin, Jian-Fang Hu, Wei-Shi Zheng, Jianhuang Lai | Video Paragraph Grounding (VPG) is an essential yet challenging task in vision-language understanding, which aims to jointly localize multiple events from an untrimmed video with a paragraph query description. One of the critical challenges in addressing this problem is to comprehend the complex semantic relations betw... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Hierarchical_Semantic_Correspondence_Networks_for_Video_Paragraph_Grounding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tan_Hierarchical_Semantic_Correspondence_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Hierarchical_Semantic_Correspondence_Networks_for_Video_Paragraph_Grounding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Hierarchical_Semantic_Correspondence_Networks_for_Video_Paragraph_Grounding_CVPR_2023_paper.html | CVPR 2023 | null |
Temporal Attention Unit: Towards Efficient Spatiotemporal Predictive Learning | Cheng Tan, Zhangyang Gao, Lirong Wu, Yongjie Xu, Jun Xia, Siyuan Li, Stan Z. Li | Spatiotemporal predictive learning aims to generate future frames by learning from historical frames. In this paper, we investigate existing methods and present a general framework of spatiotemporal predictive learning, in which the spatial encoder and decoder capture intra-frame features and the middle temporal module... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tan_Temporal_Attention_Unit_Towards_Efficient_Spatiotemporal_Predictive_Learning_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2206.12126 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Temporal_Attention_Unit_Towards_Efficient_Spatiotemporal_Predictive_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tan_Temporal_Attention_Unit_Towards_Efficient_Spatiotemporal_Predictive_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Zero-Shot Pose Transfer for Unrigged Stylized 3D Characters | Jiashun Wang, Xueting Li, Sifei Liu, Shalini De Mello, Orazio Gallo, Xiaolong Wang, Jan Kautz | Transferring the pose of a reference avatar to stylized 3D characters of various shapes is a fundamental task in computer graphics. Existing methods either require the stylized characters to be rigged, or they use the stylized character in the desired pose as ground truth at training. We present a zero-shot approach th... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Zero-Shot_Pose_Transfer_for_Unrigged_Stylized_3D_Characters_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Zero-Shot_Pose_Transfer_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Zero-Shot_Pose_Transfer_for_Unrigged_Stylized_3D_Characters_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Zero-Shot_Pose_Transfer_for_Unrigged_Stylized_3D_Characters_CVPR_2023_paper.html | CVPR 2023 | null |
Listening Human Behavior: 3D Human Pose Estimation With Acoustic Signals | Yuto Shibata, Yutaka Kawashima, Mariko Isogawa, Go Irie, Akisato Kimura, Yoshimitsu Aoki | Given only acoustic signals without any high-level information, such as voices or sounds of scenes/actions, how much can we infer about the behavior of humans? Unlike existing methods, which suffer from privacy issues because they use signals that include human speech or the sounds of specific actions, we explore how l... | https://openaccess.thecvf.com/content/CVPR2023/papers/Shibata_Listening_Human_Behavior_3D_Human_Pose_Estimation_With_Acoustic_Signals_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shibata_Listening_Human_Behavior_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shibata_Listening_Human_Behavior_3D_Human_Pose_Estimation_With_Acoustic_Signals_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shibata_Listening_Human_Behavior_3D_Human_Pose_Estimation_With_Acoustic_Signals_CVPR_2023_paper.html | CVPR 2023 | null |
Meta-Learning With a Geometry-Adaptive Preconditioner | Suhyun Kang, Duhun Hwang, Moonjung Eo, Taesup Kim, Wonjong Rhee | Model-agnostic meta-learning (MAML) is one of the most successful meta-learning algorithms. It has a bi-level optimization structure where the outer-loop process learns a shared initialization and the inner-loop process optimizes task-specific weights. Although MAML relies on the standard gradient descent in the inner-... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Meta-Learning_With_a_Geometry-Adaptive_Preconditioner_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_Meta-Learning_With_a_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Meta-Learning_With_a_Geometry-Adaptive_Preconditioner_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Meta-Learning_With_a_Geometry-Adaptive_Preconditioner_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Graph Enhanced Contrastive Learning for Chest X-Ray Report Generation | Mingjie Li, Bingqian Lin, Zicong Chen, Haokun Lin, Xiaodan Liang, Xiaojun Chang | Automatic radiology reporting has great clinical potential to relieve radiologists from heavy workloads and improve diagnosis interpretation. Recently, researchers have enhanced data-driven neural networks with medical knowledge graphs to eliminate the severe visual and textual bias in this task. The structures of such... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Dynamic_Graph_Enhanced_Contrastive_Learning_for_Chest_X-Ray_Report_Generation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Dynamic_Graph_Enhanced_Contrastive_Learning_for_Chest_X-Ray_Report_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Dynamic_Graph_Enhanced_Contrastive_Learning_for_Chest_X-Ray_Report_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
BiCro: Noisy Correspondence Rectification for Multi-Modality Data via Bi-Directional Cross-Modal Similarity Consistency | Shuo Yang, Zhaopan Xu, Kai Wang, Yang You, Hongxun Yao, Tongliang Liu, Min Xu | As one of the most fundamental techniques in multimodal learning, cross-modal matching aims to project various sensory modalities into a shared feature space. To achieve this, massive and correctly aligned data pairs are required for model training. However, unlike unimodal datasets, multimodal datasets are extremely h... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_BiCro_Noisy_Correspondence_Rectification_for_Multi-Modality_Data_via_Bi-Directional_Cross-Modal_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_BiCro_Noisy_Correspondence_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12419 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_BiCro_Noisy_Correspondence_Rectification_for_Multi-Modality_Data_via_Bi-Directional_Cross-Modal_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_BiCro_Noisy_Correspondence_Rectification_for_Multi-Modality_Data_via_Bi-Directional_Cross-Modal_CVPR_2023_paper.html | CVPR 2023 | null |
Transfer Knowledge From Head to Tail: Uncertainty Calibration Under Long-Tailed Distribution | Jiahao Chen, Bing Su | How to estimate the uncertainty of a given model is a crucial problem. Current calibration techniques treat different classes equally and thus implicitly assume that the distribution of training data is balanced, but ignore the fact that real-world data often follows a long-tailed distribution. In this paper, we explor... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Transfer_Knowledge_From_Head_to_Tail_Uncertainty_Calibration_Under_Long-Tailed_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Transfer_Knowledge_From_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06537 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Transfer_Knowledge_From_Head_to_Tail_Uncertainty_Calibration_Under_Long-Tailed_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Transfer_Knowledge_From_Head_to_Tail_Uncertainty_Calibration_Under_Long-Tailed_CVPR_2023_paper.html | CVPR 2023 | null |
FrustumFormer: Adaptive Instance-Aware Resampling for Multi-View 3D Detection | Yuqi Wang, Yuntao Chen, Zhaoxiang Zhang | The transformation of features from 2D perspective space to 3D space is essential to multi-view 3D object detection. Recent approaches mainly focus on the design of view transformation, either pixel-wisely lifting perspective view features into 3D space with estimated depth or grid-wisely constructing BEV features via ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_FrustumFormer_Adaptive_Instance-Aware_Resampling_for_Multi-View_3D_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.04467 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_FrustumFormer_Adaptive_Instance-Aware_Resampling_for_Multi-View_3D_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_FrustumFormer_Adaptive_Instance-Aware_Resampling_for_Multi-View_3D_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Global Vision Transformer Pruning With Hessian-Aware Saliency | Huanrui Yang, Hongxu Yin, Maying Shen, Pavlo Molchanov, Hai Li, Jan Kautz | Transformers yield state-of-the-art results across many tasks. However, their heuristically designed architecture impose huge computational costs during inference. This work aims on challenging the common design philosophy of the Vision Transformer (ViT) model with uniform dimension across all the stacked blocks in a m... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Global_Vision_Transformer_Pruning_With_Hessian-Aware_Saliency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Global_Vision_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2110.04869 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Global_Vision_Transformer_Pruning_With_Hessian-Aware_Saliency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Global_Vision_Transformer_Pruning_With_Hessian-Aware_Saliency_CVPR_2023_paper.html | CVPR 2023 | null |
Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed Recognition | Zhipeng Zhou, Lanqing Li, Peilin Zhao, Pheng-Ann Heng, Wei Gong | It's widely acknowledged that deep learning models with flatter minima in its loss landscape tend to generalize better. However, such property is under-explored in deep long-tailed recognition (DLTR), a practical problem where the model is required to generalize equally well across all classes when trained on highly im... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Class-Conditional_Sharpness-Aware_Minimization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Class-Conditional_Sharpness-Aware_Minimization_for_Deep_Long-Tailed_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
ScarceNet: Animal Pose Estimation With Scarce Annotations | Chen Li, Gim Hee Lee | Animal pose estimation is an important but under-explored task due to the lack of labeled data. In this paper, we tackle the task of animal pose estimation with scarce annotations, where only a small set of labeled data and unlabeled images are available. At the core of the solution to this problem setting is the use o... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_ScarceNet_Animal_Pose_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15023 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_ScarceNet_Animal_Pose_Estimation_With_Scarce_Annotations_CVPR_2023_paper.html | CVPR 2023 | null |
OmniCity: Omnipotent City Understanding With Multi-Level and Multi-View Images | Weijia Li, Yawen Lai, Linning Xu, Yuanbo Xiangli, Jinhua Yu, Conghui He, Gui-Song Xia, Dahua Lin | This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, OmniCity contains multi-view satellite images as well as street-level panorama and mono-view images, constituting over 100K pixel-wise annotated images that are well-aligned and collecte... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_OmniCity_Omnipotent_City_Understanding_With_Multi-Level_and_Multi-View_Images_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2208.00928 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_OmniCity_Omnipotent_City_Understanding_With_Multi-Level_and_Multi-View_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_OmniCity_Omnipotent_City_Understanding_With_Multi-Level_and_Multi-View_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient On-Device Training via Gradient Filtering | Yuedong Yang, Guihong Li, Radu Marculescu | Despite its importance for federated learning, continuous learning and many other applications, on-device training remains an open problem for EdgeAI. The problem stems from the large number of operations (e.g., floating point multiplications and additions) and memory consumption required during training by the back-pr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Efficient_On-Device_Training_via_Gradient_Filtering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Efficient_On-Device_Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.00330 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Efficient_On-Device_Training_via_Gradient_Filtering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Efficient_On-Device_Training_via_Gradient_Filtering_CVPR_2023_paper.html | CVPR 2023 | null |
SViTT: Temporal Learning of Sparse Video-Text Transformers | Yi Li, Kyle Min, Subarna Tripathi, Nuno Vasconcelos | Do video-text transformers learn to model temporal relationships across frames? Despite their immense capacity and the abundance of multimodal training data, recent work has revealed the strong tendency of video-text models towards frame-based spatial representations, while temporal reasoning remains largely unsolved. ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_SViTT_Temporal_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08809 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_SViTT_Temporal_Learning_of_Sparse_Video-Text_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
NeuralDome: A Neural Modeling Pipeline on Multi-View Human-Object Interactions | Juze Zhang, Haimin Luo, Hongdi Yang, Xinru Xu, Qianyang Wu, Ye Shi, Jingyi Yu, Lan Xu, Jingya Wang | Humans constantly interact with objects in daily life tasks. Capturing such processes and subsequently conducting visual inferences from a fixed viewpoint suffers from occlusions, shape and texture ambiguities, motions, etc. To mitigate the problem, it is essential to build a training dataset that captures free-viewpoi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_NeuralDome_A_Neural_Modeling_Pipeline_on_Multi-View_Human-Object_Interactions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_NeuralDome_A_Neural_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.07626 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_NeuralDome_A_Neural_Modeling_Pipeline_on_Multi-View_Human-Object_Interactions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_NeuralDome_A_Neural_Modeling_Pipeline_on_Multi-View_Human-Object_Interactions_CVPR_2023_paper.html | CVPR 2023 | null |
3D Human Mesh Estimation From Virtual Markers | Xiaoxuan Ma, Jiajun Su, Chunyu Wang, Wentao Zhu, Yizhou Wang | Inspired by the success of volumetric 3D pose estimation, some recent human mesh estimators propose to estimate 3D skeletons as intermediate representations, from which, the dense 3D meshes are regressed by exploiting the mesh topology. However, body shape information is lost in extracting skeletons, leading to mediocr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_3D_Human_Mesh_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11726 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_3D_Human_Mesh_Estimation_From_Virtual_Markers_CVPR_2023_paper.html | CVPR 2023 | null |
CUDA: Convolution-Based Unlearnable Datasets | Vinu Sankar Sadasivan, Mahdi Soltanolkotabi, Soheil Feizi | Large-scale training of modern deep learning models heavily relies on publicly available data on the web. This potentially unauthorized usage of online data leads to concerns regarding data privacy. Recent works aim to make unlearnable data for deep learning models by adding small, specially designed noises to tackle t... | https://openaccess.thecvf.com/content/CVPR2023/papers/Sadasivan_CUDA_Convolution-Based_Unlearnable_Datasets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sadasivan_CUDA_Convolution-Based_Unlearnable_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.04278 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sadasivan_CUDA_Convolution-Based_Unlearnable_Datasets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sadasivan_CUDA_Convolution-Based_Unlearnable_Datasets_CVPR_2023_paper.html | CVPR 2023 | null |
No One Left Behind: Improving the Worst Categories in Long-Tailed Learning | Yingxiao Du, Jianxin Wu | Unlike the case when using a balanced training dataset, the per-class recall (i.e., accuracy) of neural networks trained with an imbalanced dataset are known to vary a lot from category to category. The convention in long-tailed recognition is to manually split all categories into three subsets and report the average a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_No_One_Left_Behind_Improving_the_Worst_Categories_in_Long-Tailed_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_No_One_Left_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03630 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_No_One_Left_Behind_Improving_the_Worst_Categories_in_Long-Tailed_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_No_One_Left_Behind_Improving_the_Worst_Categories_in_Long-Tailed_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Fair Clustering via Maximizing and Minimizing Mutual Information: Theory, Algorithm and Metric | Pengxin Zeng, Yunfan Li, Peng Hu, Dezhong Peng, Jiancheng Lv, Xi Peng | Fair clustering aims to divide data into distinct clusters while preventing sensitive attributes (e.g., gender, race, RNA sequencing technique) from dominating the clustering. Although a number of works have been conducted and achieved huge success recently, most of them are heuristical, and there lacks a unified theor... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zeng_Deep_Fair_Clustering_via_Maximizing_and_Minimizing_Mutual_Information_Theory_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zeng_Deep_Fair_Clustering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.12396 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Deep_Fair_Clustering_via_Maximizing_and_Minimizing_Mutual_Information_Theory_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zeng_Deep_Fair_Clustering_via_Maximizing_and_Minimizing_Mutual_Information_Theory_CVPR_2023_paper.html | CVPR 2023 | null |
MIANet: Aggregating Unbiased Instance and General Information for Few-Shot Semantic Segmentation | Yong Yang, Qiong Chen, Yuan Feng, Tianlin Huang | Existing few-shot segmentation methods are based on the meta-learning strategy and extract instance knowledge from a support set and then apply the knowledge to segment target objects in a query set. However, the extracted knowledge is insufficient to cope with the variable intra-class differences since the knowledge i... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_MIANet_Aggregating_Unbiased_Instance_and_General_Information_for_Few-Shot_Semantic_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_MIANet_Aggregating_Unbiased_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_MIANet_Aggregating_Unbiased_Instance_and_General_Information_for_Few-Shot_Semantic_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_MIANet_Aggregating_Unbiased_Instance_and_General_Information_for_Few-Shot_Semantic_CVPR_2023_paper.html | CVPR 2023 | null |
High Fidelity 3D Hand Shape Reconstruction via Scalable Graph Frequency Decomposition | Tianyu Luan, Yuanhao Zhai, Jingjing Meng, Zhong Li, Zhang Chen, Yi Xu, Junsong Yuan | Despite the impressive performance obtained by recent single-image hand modeling techniques, they lack the capability to capture sufficient details of the 3D hand mesh. This deficiency greatly limits their applications when high fidelity hand modeling is required, e.g., personalized hand modeling. To address this probl... | https://openaccess.thecvf.com/content/CVPR2023/papers/Luan_High_Fidelity_3D_Hand_Shape_Reconstruction_via_Scalable_Graph_Frequency_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luan_High_Fidelity_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luan_High_Fidelity_3D_Hand_Shape_Reconstruction_via_Scalable_Graph_Frequency_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luan_High_Fidelity_3D_Hand_Shape_Reconstruction_via_Scalable_Graph_Frequency_CVPR_2023_paper.html | CVPR 2023 | null |
COT: Unsupervised Domain Adaptation With Clustering and Optimal Transport | Yang Liu, Zhipeng Zhou, Baigui Sun | Unsupervised domain adaptation (UDA) aims to transfer the knowledge from a labeled source domain to an unlabeled target domain. Typically, to guarantee desirable knowledge transfer, aligning the distribution between source and target domain from a global perspective is widely adopted in UDA. Recent researchers further ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_COT_Unsupervised_Domain_Adaptation_With_Clustering_and_Optimal_Transport_CVPR_2023_paper.html | CVPR 2023 | null |
Target-Referenced Reactive Grasping for Dynamic Objects | Jirong Liu, Ruo Zhang, Hao-Shu Fang, Minghao Gou, Hongjie Fang, Chenxi Wang, Sheng Xu, Hengxu Yan, Cewu Lu | Reactive grasping, which enables the robot to successfully grasp dynamic moving objects, is of great interest in robotics. Current methods mainly focus on the temporal smoothness of the predicted grasp poses but few consider their semantic consistency. Consequently, the predicted grasps are not guaranteed to fall on th... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Target-Referenced_Reactive_Grasping_for_Dynamic_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Target-Referenced_Reactive_Grasping_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Target-Referenced_Reactive_Grasping_for_Dynamic_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Target-Referenced_Reactive_Grasping_for_Dynamic_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Exploit the Sequence-Specific Prior Knowledge for Image Processing Pipelines Optimization | Haina Qin, Longfei Han, Weihua Xiong, Juan Wang, Wentao Ma, Bing Li, Weiming Hu | The hardware image signal processing (ISP) pipeline is the intermediate layer between the imaging sensor and the downstream application, processing the sensor signal into an RGB image. The ISP is less programmable and consists of a series of processing modules. Each processing module handles a subtask and contains a se... | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qin_Learning_To_Exploit_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Learning_To_Exploit_the_Sequence-Specific_Prior_Knowledge_for_Image_Processing_CVPR_2023_paper.html | CVPR 2023 | null |
Complexity-Guided Slimmable Decoder for Efficient Deep Video Compression | Zhihao Hu, Dong Xu | In this work, we propose the complexity-guided slimmable decoder (cgSlimDecoder) in combination with skip-adaptive entropy coding (SaEC) for efficient deep video compression. Specifically, given the target complexity constraints, in our cgSlimDecoder, we introduce a set of new channel width selection modules to automat... | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hu_Complexity-Guided_Slimmable_Decoder_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Complexity-Guided_Slimmable_Decoder_for_Efficient_Deep_Video_Compression_CVPR_2023_paper.html | CVPR 2023 | null |
Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimation | Ning Zhang, Francesco Nex, George Vosselman, Norman Kerle | Self-supervised monocular depth estimation that does not require ground truth for training has attracted attention in recent years. It is of high interest to design lightweight but effective models so that they can be deployed on edge devices. Many existing architectures benefit from using heavier backbones at the expe... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Lite-Mono_A_Lightweight_CNN_and_Transformer_Architecture_for_Self-Supervised_Monocular_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Lite-Mono_A_Lightweight_CNN_and_Transformer_Architecture_for_Self-Supervised_Monocular_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Lite-Mono_A_Lightweight_CNN_and_Transformer_Architecture_for_Self-Supervised_Monocular_CVPR_2023_paper.html | CVPR 2023 | null |
MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins | Tiberiu Sosea, Cornelia Caragea | We introduce MarginMatch, a new SSL approach combining consistency regularization and pseudo-labeling, with its main novelty arising from the use of unlabeled data training dynamics to measure pseudo-label quality. Instead of using only the model's confidence on an unlabeled example at an arbitrary iteration to decide ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Sosea_MarginMatch_Improving_Semi-Supervised_Learning_with_Pseudo-Margins_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sosea_MarginMatch_Improving_Semi-Supervised_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sosea_MarginMatch_Improving_Semi-Supervised_Learning_with_Pseudo-Margins_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sosea_MarginMatch_Improving_Semi-Supervised_Learning_with_Pseudo-Margins_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Scene Chronology | Haotong Lin, Qianqian Wang, Ruojin Cai, Sida Peng, Hadar Averbuch-Elor, Xiaowei Zhou, Noah Snavely | In this work, we aim to reconstruct a time-varying 3D model, capable of rendering photo-realistic renderings with independent control of viewpoint, illumination, and time, from Internet photos of large-scale landmarks. The core challenges are twofold. First, different types of temporal changes, such as illumination and... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Neural_Scene_Chronology_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Neural_Scene_Chronology_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Neural_Scene_Chronology_CVPR_2023_paper.html | CVPR 2023 | null |
Starting From Non-Parametric Networks for 3D Point Cloud Analysis | Renrui Zhang, Liuhui Wang, Yali Wang, Peng Gao, Hongsheng Li, Jianbo Shi | We present a Non-parametric Network for 3D point cloud analysis, Point-NN, which consists of purely non-learnable components: farthest point sampling (FPS), k-nearest neighbors (k-NN), and pooling operations, with trigonometric functions. Surprisingly, it performs well on various 3D tasks, requiring no parameters or tr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Starting_From_Non-Parametric_Networks_for_3D_Point_Cloud_Analysis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Starting_From_Non-Parametric_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08134 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Starting_From_Non-Parametric_Networks_for_3D_Point_Cloud_Analysis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Starting_From_Non-Parametric_Networks_for_3D_Point_Cloud_Analysis_CVPR_2023_paper.html | CVPR 2023 | null |
Light Source Separation and Intrinsic Image Decomposition Under AC Illumination | Yusaku Yoshida, Ryo Kawahara, Takahiro Okabe | Artificial light sources are often powered by an electric grid, and then their intensities rapidly oscillate in response to the grid's alternating current (AC). Interestingly, the flickers of scene radiance values due to AC illumination are useful for extracting rich information on a scene of interest. In this paper, w... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yoshida_Light_Source_Separation_and_Intrinsic_Image_Decomposition_Under_AC_Illumination_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yoshida_Light_Source_Separation_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yoshida_Light_Source_Separation_and_Intrinsic_Image_Decomposition_Under_AC_Illumination_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yoshida_Light_Source_Separation_and_Intrinsic_Image_Decomposition_Under_AC_Illumination_CVPR_2023_paper.html | CVPR 2023 | null |
TIPI: Test Time Adaptation With Transformation Invariance | A. Tuan Nguyen, Thanh Nguyen-Tang, Ser-Nam Lim, Philip H.S. Torr | When deploying a machine learning model to a new environment, we often encounter the distribution shift problem -- meaning the target data distribution is different from the model's training distribution. In this paper, we assume that labels are not provided for this new domain, and that we do not store the source data... | https://openaccess.thecvf.com/content/CVPR2023/papers/Nguyen_TIPI_Test_Time_Adaptation_With_Transformation_Invariance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nguyen_TIPI_Test_Time_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_TIPI_Test_Time_Adaptation_With_Transformation_Invariance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_TIPI_Test_Time_Adaptation_With_Transformation_Invariance_CVPR_2023_paper.html | CVPR 2023 | null |
OTAvatar: One-Shot Talking Face Avatar With Controllable Tri-Plane Rendering | Zhiyuan Ma, Xiangyu Zhu, Guo-Jun Qi, Zhen Lei, Lei Zhang | Controllability, generalizability and efficiency are the major objectives of constructing face avatars represented by neural implicit field. However, existing methods have not managed to accommodate the three requirements simultaneously. They either focus on static portraits, restricting the representation ability to a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_OTAvatar_One-Shot_Talking_Face_Avatar_With_Controllable_Tri-Plane_Rendering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_OTAvatar_One-Shot_Talking_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14662 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_OTAvatar_One-Shot_Talking_Face_Avatar_With_Controllable_Tri-Plane_Rendering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_OTAvatar_One-Shot_Talking_Face_Avatar_With_Controllable_Tri-Plane_Rendering_CVPR_2023_paper.html | CVPR 2023 | null |
Beyond Appearance: A Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks | Weihua Chen, Xianzhe Xu, Jian Jia, Hao Luo, Yaohua Wang, Fan Wang, Rong Jin, Xiuyu Sun | Human-centric visual tasks have attracted increasing research attention due to their widespread applications. In this paper, we aim to learn a general human representation from massive unlabeled human images which can benefit downstream human-centric tasks to the maximum extent. We call this method SOLIDER, a Semantic ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Beyond_Appearance_A_Semantic_Controllable_Self-Supervised_Learning_Framework_for_Human-Centric_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Beyond_Appearance_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17602 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Beyond_Appearance_A_Semantic_Controllable_Self-Supervised_Learning_Framework_for_Human-Centric_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Beyond_Appearance_A_Semantic_Controllable_Self-Supervised_Learning_Framework_for_Human-Centric_CVPR_2023_paper.html | CVPR 2023 | null |
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