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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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SCPNet: Semantic Scene Completion on Point Cloud | Zhaoyang Xia, Youquan Liu, Xin Li, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao | Training deep models for semantic scene completion is challenging due to the sparse and incomplete input, a large quantity of objects of diverse scales as well as the inherent label noise for moving objects. To address the above-mentioned problems, we propose the following three solutions: 1) Redesigning the completion... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xia_SCPNet_Semantic_Scene_Completion_on_Point_Cloud_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xia_SCPNet_Semantic_Scene_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06884 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_SCPNet_Semantic_Scene_Completion_on_Point_Cloud_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xia_SCPNet_Semantic_Scene_Completion_on_Point_Cloud_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Prototypical Network for Cross Domain Few-Shot Learning | Fei Zhou, Peng Wang, Lei Zhang, Wei Wei, Yanning Zhang | Prototypical Network is a popular few-shot solver that aims at establishing a feature metric generalizable to novel few-shot classification (FSC) tasks using deep neural networks. However, its performance drops dramatically when generalizing to the FSC tasks in new domains. In this study, we revisit this problem and ar... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Revisiting_Prototypical_Network_for_Cross_Domain_Few-Shot_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Revisiting_Prototypical_Network_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Revisiting_Prototypical_Network_for_Cross_Domain_Few-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Revisiting_Prototypical_Network_for_Cross_Domain_Few-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
QPGesture: Quantization-Based and Phase-Guided Motion Matching for Natural Speech-Driven Gesture Generation | Sicheng Yang, Zhiyong Wu, Minglei Li, Zhensong Zhang, Lei Hao, Weihong Bao, Haolin Zhuang | Speech-driven gesture generation is highly challenging due to the random jitters of human motion. In addition, there is an inherent asynchronous relationship between human speech and gestures. To tackle these challenges, we introduce a novel quantization-based and phase-guided motion matching framework. Specifically, w... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_QPGesture_Quantization-Based_and_Phase-Guided_Motion_Matching_for_Natural_Speech-Driven_Gesture_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_QPGesture_Quantization-Based_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_QPGesture_Quantization-Based_and_Phase-Guided_Motion_Matching_for_Natural_Speech-Driven_Gesture_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_QPGesture_Quantization-Based_and_Phase-Guided_Motion_Matching_for_Natural_Speech-Driven_Gesture_CVPR_2023_paper.html | CVPR 2023 | null |
Multiscale Tensor Decomposition and Rendering Equation Encoding for View Synthesis | Kang Han, Wei Xiang | Rendering novel views from captured multi-view images has made considerable progress since the emergence of the neural radiance field. This paper aims to further advance the quality of view rendering by proposing a novel approach dubbed the neural radiance feature field (NRFF). We first propose a multiscale tensor deco... | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_Multiscale_Tensor_Decomposition_and_Rendering_Equation_Encoding_for_View_Synthesis_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_Multiscale_Tensor_Decomposition_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Multiscale_Tensor_Decomposition_and_Rendering_Equation_Encoding_for_View_Synthesis_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_Multiscale_Tensor_Decomposition_and_Rendering_Equation_Encoding_for_View_Synthesis_CVPR_2023_paper.html | CVPR 2023 | null |
NS3D: Neuro-Symbolic Grounding of 3D Objects and Relations | Joy Hsu, Jiayuan Mao, Jiajun Wu | Grounding object properties and relations in 3D scenes is a prerequisite for a wide range of artificial intelligence tasks, such as visually grounded dialogues and embodied manipulation. However, the variability of the 3D domain induces two fundamental challenges: 1) the expense of labeling and 2) the complexity of 3D ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hsu_NS3D_Neuro-Symbolic_Grounding_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13483 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_NS3D_Neuro-Symbolic_Grounding_of_3D_Objects_and_Relations_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Accurate 3D Shape Based on Stereo Polarimetric Imaging | Tianyu Huang, Haoang Li, Kejing He, Congying Sui, Bin Li, Yun-Hui Liu | Shape from Polarization (SfP) aims to recover surface normal using the polarization cues of light. The accuracy of existing SfP methods is affected by two main problems. First, the ambiguity of polarization cues partially results in false normal estimation. Second, the widely-used assumption about orthographic projecti... | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Learning_Accurate_3D_Shape_Based_on_Stereo_Polarimetric_Imaging_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Learning_Accurate_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Learning_Accurate_3D_Shape_Based_on_Stereo_Polarimetric_Imaging_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Learning_Accurate_3D_Shape_Based_on_Stereo_Polarimetric_Imaging_CVPR_2023_paper.html | CVPR 2023 | null |
VideoMAE V2: Scaling Video Masked Autoencoders With Dual Masking | Limin Wang, Bingkun Huang, Zhiyu Zhao, Zhan Tong, Yinan He, Yi Wang, Yali Wang, Yu Qiao | Scale is the primary factor for building a powerful foundation model that could well generalize to a variety of downstream tasks. However, it is still challenging to train video foundation models with billions of parameters. This paper shows that video masked autoencoder (VideoMAE) is a scalable and general self-superv... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_VideoMAE_V2_Scaling_Video_Masked_Autoencoders_With_Dual_Masking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_VideoMAE_V2_Scaling_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16727 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_VideoMAE_V2_Scaling_Video_Masked_Autoencoders_With_Dual_Masking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_VideoMAE_V2_Scaling_Video_Masked_Autoencoders_With_Dual_Masking_CVPR_2023_paper.html | CVPR 2023 | null |
GANmouflage: 3D Object Nondetection With Texture Fields | Rui Guo, Jasmine Collins, Oscar de Lima, Andrew Owens | We propose a method that learns to camouflage 3D objects within scenes. Given an object's shape and a distribution of viewpoints from which it will be seen, we estimate a texture that will make it difficult to detect. Successfully solving this task requires a model that can accurately reproduce textures from the scene,... | https://openaccess.thecvf.com/content/CVPR2023/papers/Guo_GANmouflage_3D_Object_Nondetection_With_Texture_Fields_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2201.07202 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_GANmouflage_3D_Object_Nondetection_With_Texture_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Guo_GANmouflage_3D_Object_Nondetection_With_Texture_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Perception and Semantic Aware Regularization for Sequential Confidence Calibration | Zhenghua Peng, Yu Luo, Tianshui Chen, Keke Xu, Shuangping Huang | Deep sequence recognition (DSR) models receive increasing attention due to their superior application to various applications. Most DSR models use merely the target sequences as supervision without considering other related sequences, leading to over-confidence in their predictions. The DSR models trained with label sm... | https://openaccess.thecvf.com/content/CVPR2023/papers/Peng_Perception_and_Semantic_Aware_Regularization_for_Sequential_Confidence_Calibration_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Peng_Perception_and_Semantic_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Perception_and_Semantic_Aware_Regularization_for_Sequential_Confidence_Calibration_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Peng_Perception_and_Semantic_Aware_Regularization_for_Sequential_Confidence_Calibration_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Residual Networks for Adversarial Robustness | Shihua Huang, Zhichao Lu, Kalyanmoy Deb, Vishnu Naresh Boddeti | Efforts to improve the adversarial robustness of convolutional neural networks have primarily focused on developing more effective adversarial training methods. In contrast, little attention was devoted to analyzing the role of architectural elements (e.g., topology, depth, and width) on adversarial robustness. This pa... | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Revisiting_Residual_Networks_for_Adversarial_Robustness_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Revisiting_Residual_Networks_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.11005 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Revisiting_Residual_Networks_for_Adversarial_Robustness_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Revisiting_Residual_Networks_for_Adversarial_Robustness_CVPR_2023_paper.html | CVPR 2023 | null |
RA-CLIP: Retrieval Augmented Contrastive Language-Image Pre-Training | Chen-Wei Xie, Siyang Sun, Xiong Xiong, Yun Zheng, Deli Zhao, Jingren Zhou | Contrastive Language-Image Pre-training (CLIP) is attracting increasing attention for its impressive zero-shot recognition performance on different down-stream tasks. However, training CLIP is data-hungry and requires lots of image-text pairs to memorize various semantic concepts. In this paper, we propose a novel and ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_RA-CLIP_Retrieval_Augmented_Contrastive_Language-Image_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_RA-CLIP_Retrieval_Augmented_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_RA-CLIP_Retrieval_Augmented_Contrastive_Language-Image_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_RA-CLIP_Retrieval_Augmented_Contrastive_Language-Image_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
PosterLayout: A New Benchmark and Approach for Content-Aware Visual-Textual Presentation Layout | Hsiao Yuan Hsu, Xiangteng He, Yuxin Peng, Hao Kong, Qing Zhang | Content-aware visual-textual presentation layout aims at arranging spatial space on the given canvas for pre-defined elements, including text, logo, and underlay, which is a key to automatic template-free creative graphic design. In practical applications, e.g., poster designs, the canvas is originally non-empty, and b... | https://openaccess.thecvf.com/content/CVPR2023/papers/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.15937 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hsu_PosterLayout_A_New_Benchmark_and_Approach_for_Content-Aware_Visual-Textual_Presentation_CVPR_2023_paper.html | CVPR 2023 | null |
A Practical Upper Bound for the Worst-Case Attribution Deviations | Fan Wang, Adams Wai-Kin Kong | Model attribution is a critical component of deep neural networks (DNNs) for its interpretability to complex models. Recent studies bring up attention to the security of attribution methods as they are vulnerable to attribution attacks that generate similar images with dramatically different attributions. Existing work... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_A_Practical_Upper_Bound_for_the_Worst-Case_Attribution_Deviations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_A_Practical_Upper_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.00340 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_A_Practical_Upper_Bound_for_the_Worst-Case_Attribution_Deviations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_A_Practical_Upper_Bound_for_the_Worst-Case_Attribution_Deviations_CVPR_2023_paper.html | CVPR 2023 | null |
A General Regret Bound of Preconditioned Gradient Method for DNN Training | Hongwei Yong, Ying Sun, Lei Zhang | While adaptive learning rate methods, such as Adam, have achieved remarkable improvement in optimizing Deep Neural Networks (DNNs), they consider only the diagonal elements of the full preconditioned matrix. Though the full-matrix preconditioned gradient methods theoretically have a lower regret bound, they are impract... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yong_A_General_Regret_Bound_of_Preconditioned_Gradient_Method_for_DNN_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yong_A_General_Regret_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yong_A_General_Regret_Bound_of_Preconditioned_Gradient_Method_for_DNN_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yong_A_General_Regret_Bound_of_Preconditioned_Gradient_Method_for_DNN_CVPR_2023_paper.html | CVPR 2023 | null |
Teacher-Generated Spatial-Attention Labels Boost Robustness and Accuracy of Contrastive Models | Yushi Yao, Chang Ye, Junfeng He, Gamaleldin F. Elsayed | Human spatial attention conveys information about theregions of visual scenes that are important for perform-ing visual tasks. Prior work has shown that the informa-tion about human attention can be leveraged to benefit var-ious supervised vision tasks. Might providing this weakform of supervision be useful for self-su... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Teacher-Generated_Spatial-Attention_Labels_Boost_Robustness_and_Accuracy_of_Contrastive_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_Teacher-Generated_Spatial-Attention_Labels_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Teacher-Generated_Spatial-Attention_Labels_Boost_Robustness_and_Accuracy_of_Contrastive_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Teacher-Generated_Spatial-Attention_Labels_Boost_Robustness_and_Accuracy_of_Contrastive_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring and Exploiting Uncertainty for Incomplete Multi-View Classification | Mengyao Xie, Zongbo Han, Changqing Zhang, Yichen Bai, Qinghua Hu | Classifying incomplete multi-view data is inevitable since arbitrary view missing widely exists in real-world applications. Although great progress has been achieved, existing incomplete multi-view methods are still difficult to obtain a trustworthy prediction due to the relatively high uncertainty nature of missing vi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_Exploring_and_Exploiting_Uncertainty_for_Incomplete_Multi-View_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_Exploring_and_Exploiting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05165 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Exploring_and_Exploiting_Uncertainty_for_Incomplete_Multi-View_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_Exploring_and_Exploiting_Uncertainty_for_Incomplete_Multi-View_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning | Antoine Yang, Arsha Nagrani, Paul Hongsuck Seo, Antoine Miech, Jordi Pont-Tuset, Ivan Laptev, Josef Sivic, Cordelia Schmid | In this work, we introduce Vid2Seq, a multi-modal single-stage dense event captioning model pretrained on narrated videos which are readily-available at scale. The Vid2Seq architecture augments a language model with special time tokens, allowing it to seamlessly predict event boundaries and textual descriptions in the ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Vid2Seq_Large-Scale_Pretraining_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14115 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Vid2Seq_Large-Scale_Pretraining_of_a_Visual_Language_Model_for_Dense_CVPR_2023_paper.html | CVPR 2023 | null |
Optimal Proposal Learning for Deployable End-to-End Pedestrian Detection | Xiaolin Song, Binghui Chen, Pengyu Li, Jun-Yan He, Biao Wang, Yifeng Geng, Xuansong Xie, Honggang Zhang | End-to-end pedestrian detection focuses on training a pedestrian detection model via discarding the Non-Maximum Suppression (NMS) post-processing. Though a few methods have been explored, most of them still suffer from longer training time and more complex deployment, which cannot be deployed in the actual industrial a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_Optimal_Proposal_Learning_for_Deployable_End-to-End_Pedestrian_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Discovering the Real Association: Multimodal Causal Reasoning in Video Question Answering | Chuanqi Zang, Hanqing Wang, Mingtao Pei, Wei Liang | Video Question Answering (VideoQA) is challenging as it requires capturing accurate correlations between modalities from redundant information. Recent methods focus on the explicit challenges of the task, e.g. multimodal feature extraction, video-text alignment and fusion. Their frameworks reason the answer relying on ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zang_Discovering_the_Real_Association_Multimodal_Causal_Reasoning_in_Video_Question_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zang_Discovering_the_Real_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zang_Discovering_the_Real_Association_Multimodal_Causal_Reasoning_in_Video_Question_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zang_Discovering_the_Real_Association_Multimodal_Causal_Reasoning_in_Video_Question_CVPR_2023_paper.html | CVPR 2023 | null |
Temporal Interpolation Is All You Need for Dynamic Neural Radiance Fields | Sungheon Park, Minjung Son, Seokhwan Jang, Young Chun Ahn, Ji-Yeon Kim, Nahyup Kang | Temporal interpolation often plays a crucial role to learn meaningful representations in dynamic scenes. In this paper, we propose a novel method to train spatiotemporal neural radiance fields of dynamic scenes based on temporal interpolation of feature vectors. Two feature interpolation methods are suggested depending... | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Temporal_Interpolation_Is_All_You_Need_for_Dynamic_Neural_Radiance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Temporal_Interpolation_Is_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.09311 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Temporal_Interpolation_Is_All_You_Need_for_Dynamic_Neural_Radiance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Temporal_Interpolation_Is_All_You_Need_for_Dynamic_Neural_Radiance_CVPR_2023_paper.html | CVPR 2023 | null |
Graph Transformer GANs for Graph-Constrained House Generation | Hao Tang, Zhenyu Zhang, Humphrey Shi, Bo Li, Ling Shao, Nicu Sebe, Radu Timofte, Luc Van Gool | We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for the challenging graph-constrained house generation task. The proposed graph-Transformer-based generator includes a novel graph Transformer encoder that combines graph convolut... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_Graph_Transformer_GANs_for_Graph-Constrained_House_Generation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08225 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Graph_Transformer_GANs_for_Graph-Constrained_House_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_Graph_Transformer_GANs_for_Graph-Constrained_House_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
On the Benefits of 3D Pose and Tracking for Human Action Recognition | Jathushan Rajasegaran, Georgios Pavlakos, Angjoo Kanazawa, Christoph Feichtenhofer, Jitendra Malik | In this work we study the benefits of using tracking and 3D poses for action recognition. To achieve this, we take the Lagrangian view on analysing actions over a trajectory of human motion rather than at a fixed point in space. Taking this stand allows us to use the tracklets of people to predict their actions. In thi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Rajasegaran_On_the_Benefits_of_3D_Pose_and_Tracking_for_Human_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rajasegaran_On_the_Benefits_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01199 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rajasegaran_On_the_Benefits_of_3D_Pose_and_Tracking_for_Human_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rajasegaran_On_the_Benefits_of_3D_Pose_and_Tracking_for_Human_CVPR_2023_paper.html | CVPR 2023 | null |
How to Backdoor Diffusion Models? | Sheng-Yen Chou, Pin-Yu Chen, Tsung-Yi Ho | Diffusion models are state-of-the-art deep learning empowered generative models that are trained based on the principle of learning forward and reverse diffusion processes via progressive noise-addition and denoising. To gain a better understanding of the limitations and potential risks, this paper presents the first s... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chou_How_to_Backdoor_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chou_How_to_Backdoor_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05400 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chou_How_to_Backdoor_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chou_How_to_Backdoor_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
ERNIE-ViLG 2.0: Improving Text-to-Image Diffusion Model With Knowledge-Enhanced Mixture-of-Denoising-Experts | Zhida Feng, Zhenyu Zhang, Xintong Yu, Yewei Fang, Lanxin Li, Xuyi Chen, Yuxiang Lu, Jiaxiang Liu, Weichong Yin, Shikun Feng, Yu Sun, Li Chen, Hao Tian, Hua Wu, Haifeng Wang | Recent progress in diffusion models has revolutionized the popular technology of text-to-image generation. While existing approaches could produce photorealistic high-resolution images with text conditions, there are still several open problems to be solved, which limits the further improvement of image fidelity and te... | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_ERNIE-ViLG_2.0_Improving_Text-to-Image_Diffusion_Model_With_Knowledge-Enhanced_Mixture-of-Denoising-Experts_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_ERNIE-ViLG_2.0_Improving_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_ERNIE-ViLG_2.0_Improving_Text-to-Image_Diffusion_Model_With_Knowledge-Enhanced_Mixture-of-Denoising-Experts_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_ERNIE-ViLG_2.0_Improving_Text-to-Image_Diffusion_Model_With_Knowledge-Enhanced_Mixture-of-Denoising-Experts_CVPR_2023_paper.html | CVPR 2023 | null |
PACO: Parts and Attributes of Common Objects | Vignesh Ramanathan, Anmol Kalia, Vladan Petrovic, Yi Wen, Baixue Zheng, Baishan Guo, Rui Wang, Aaron Marquez, Rama Kovvuri, Abhishek Kadian, Amir Mousavi, Yiwen Song, Abhimanyu Dubey, Dhruv Mahajan | Object models are gradually progressing from predicting just category labels to providing detailed descriptions of object instances. This motivates the need for large datasets which go beyond traditional object masks and provide richer annotations such as part masks and attributes. Hence, we introduce PACO: Parts and A... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ramanathan_PACO_Parts_and_Attributes_of_Common_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ramanathan_PACO_Parts_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01795 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ramanathan_PACO_Parts_and_Attributes_of_Common_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ramanathan_PACO_Parts_and_Attributes_of_Common_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Transformations To Reduce the Geometric Shift in Object Detection | Vidit Vidit, Martin Engilberge, Mathieu Salzmann | The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between synthetic and real images. Here, by contrast, we tackle geometric shifts... | https://openaccess.thecvf.com/content/CVPR2023/papers/Vidit_Learning_Transformations_To_Reduce_the_Geometric_Shift_in_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Vidit_Learning_Transformations_To_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.05496 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Vidit_Learning_Transformations_To_Reduce_the_Geometric_Shift_in_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Vidit_Learning_Transformations_To_Reduce_the_Geometric_Shift_in_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
OReX: Object Reconstruction From Planar Cross-Sections Using Neural Fields | Haim Sawdayee, Amir Vaxman, Amit H. Bermano | Reconstructing 3D shapes from planar cross-sections is a challenge inspired by downstream applications like medical imaging and geographic informatics. The input is an in/out indicator function fully defined on a sparse collection of planes in space, and the output is an interpolation of the indicator function to the e... | https://openaccess.thecvf.com/content/CVPR2023/papers/Sawdayee_OReX_Object_Reconstruction_From_Planar_Cross-Sections_Using_Neural_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sawdayee_OReX_Object_Reconstruction_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12886 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sawdayee_OReX_Object_Reconstruction_From_Planar_Cross-Sections_Using_Neural_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sawdayee_OReX_Object_Reconstruction_From_Planar_Cross-Sections_Using_Neural_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
SPIn-NeRF: Multiview Segmentation and Perceptual Inpainting With Neural Radiance Fields | Ashkan Mirzaei, Tristan Aumentado-Armstrong, Konstantinos G. Derpanis, Jonathan Kelly, Marcus A. Brubaker, Igor Gilitschenski, Alex Levinshtein | Neural Radiance Fields (NeRFs) have emerged as a popular approach for novel view synthesis. While NeRFs are quickly being adapted for a wider set of applications, intuitively editing NeRF scenes is still an open challenge. One important editing task is the removal of unwanted objects from a 3D scene, such that the repl... | https://openaccess.thecvf.com/content/CVPR2023/papers/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mirzaei_SPIn-NeRF_Multiview_Segmentation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mirzaei_SPIn-NeRF_Multiview_Segmentation_and_Perceptual_Inpainting_With_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting the Stack-Based Inverse Tone Mapping | Ning Zhang, Yuyao Ye, Yang Zhao, Ronggang Wang | Current stack-based inverse tone mapping (ITM) methods can recover high dynamic range (HDR) radiance by predicting a set of multi-exposure images from a single low dynamic range image. However, there are still some limitations. On the one hand, these methods estimate a fixed number of images (e.g., three exposure-up an... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Revisiting_the_Stack-Based_Inverse_Tone_Mapping_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Revisiting_the_Stack-Based_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Revisiting_the_Stack-Based_Inverse_Tone_Mapping_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Revisiting_the_Stack-Based_Inverse_Tone_Mapping_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Rotation Averaging: Uncertainties and Robust Losses | Ganlin Zhang, Viktor Larsson, Daniel Barath | In this paper, we revisit the rotation averaging problem applied in global Structure-from-Motion pipelines. We argue that the main problem of current methods is the minimized cost function that is only weakly connected with the input data via the estimated epipolar geometries. We propose to better model the underlying ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Revisiting_Rotation_Averaging_Uncertainties_and_Robust_Losses_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Revisiting_Rotation_Averaging_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05195 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Revisiting_Rotation_Averaging_Uncertainties_and_Robust_Losses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Revisiting_Rotation_Averaging_Uncertainties_and_Robust_Losses_CVPR_2023_paper.html | CVPR 2023 | null |
Continuous Sign Language Recognition With Correlation Network | Lianyu Hu, Liqing Gao, Zekang Liu, Wei Feng | Human body trajectories are a salient cue to identify actions in video. Such body trajectories are mainly conveyed by hands and face across consecutive frames in sign language. However, current methods in continuous sign language recognition(CSLR) usually process frames independently to capture frame-wise features, thu... | https://openaccess.thecvf.com/content/CVPR2023/papers/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.03202 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hu_Continuous_Sign_Language_Recognition_With_Correlation_Network_CVPR_2023_paper.html | CVPR 2023 | null |
A Simple Framework for Text-Supervised Semantic Segmentation | Muyang Yi, Quan Cui, Hao Wu, Cheng Yang, Osamu Yoshie, Hongtao Lu | Text-supervised semantic segmentation is a novel research topic that allows semantic segments to emerge with image-text contrasting. However, pioneering methods could be subject to specifically designed network architectures. This paper shows that a vanilla contrastive language-image pre-training (CLIP) model is an eff... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yi_A_Simple_Framework_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yi_A_Simple_Framework_for_Text-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Exploiting Completeness and Uncertainty of Pseudo Labels for Weakly Supervised Video Anomaly Detection | Chen Zhang, Guorong Li, Yuankai Qi, Shuhui Wang, Laiyun Qing, Qingming Huang, Ming-Hsuan Yang | Weakly supervised video anomaly detection aims to identify abnormal events in videos using only video-level labels. Recently, two-stage self-training methods have achieved significant improvements by self-generating pseudo labels and self-refining anomaly scores with these labels. As the pseudo labels play a crucial ro... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Exploiting_Completeness_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04090 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Exploiting_Completeness_and_Uncertainty_of_Pseudo_Labels_for_Weakly_Supervised_CVPR_2023_paper.html | CVPR 2023 | null |
PlenVDB: Memory Efficient VDB-Based Radiance Fields for Fast Training and Rendering | Han Yan, Celong Liu, Chao Ma, Xing Mei | In this paper, we present a new representation for neural radiance fields that accelerates both the training and the inference processes with VDB, a hierarchical data structure for sparse volumes. VDB takes both the advantages of sparse and dense volumes for compact data representation and efficient data access, being ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_PlenVDB_Memory_Efficient_VDB-Based_Radiance_Fields_for_Fast_Training_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yan_PlenVDB_Memory_Efficient_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_PlenVDB_Memory_Efficient_VDB-Based_Radiance_Fields_for_Fast_Training_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_PlenVDB_Memory_Efficient_VDB-Based_Radiance_Fields_for_Fast_Training_and_CVPR_2023_paper.html | CVPR 2023 | null |
Patch-Based 3D Natural Scene Generation From a Single Example | Weiyu Li, Xuelin Chen, Jue Wang, Baoquan Chen | We target a 3D generative model for general natural scenes that are typically unique and intricate. Lacking the necessary volumes of training data, along with the difficulties of having ad hoc designs in presence of varying scene characteristics, renders existing setups intractable. Inspired by classical patch-based im... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Patch-Based_3D_Natural_Scene_Generation_From_a_Single_Example_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Patch-Based_3D_Natural_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12670 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Patch-Based_3D_Natural_Scene_Generation_From_a_Single_Example_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Patch-Based_3D_Natural_Scene_Generation_From_a_Single_Example_CVPR_2023_paper.html | CVPR 2023 | null |
Full or Weak Annotations? An Adaptive Strategy for Budget-Constrained Annotation Campaigns | Javier Gamazo Tejero, Martin S. Zinkernagel, Sebastian Wolf, Raphael Sznitman, Pablo Márquez-Neila | Annotating new datasets for machine learning tasks is tedious, time-consuming, and costly. For segmentation applications, the burden is particularly high as manual delineations of relevant image content are often extremely expensive or can only be done by experts with domain-specific knowledge. Thanks to developments i... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tejero_Full_or_Weak_Annotations_An_Adaptive_Strategy_for_Budget-Constrained_Annotation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tejero_Full_or_Weak_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11678 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tejero_Full_or_Weak_Annotations_An_Adaptive_Strategy_for_Budget-Constrained_Annotation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tejero_Full_or_Weak_Annotations_An_Adaptive_Strategy_for_Budget-Constrained_Annotation_CVPR_2023_paper.html | CVPR 2023 | null |
Leveraging Hidden Positives for Unsupervised Semantic Segmentation | Hyun Seok Seong, WonJun Moon, SuBeen Lee, Jae-Pil Heo | Dramatic demand for manpower to label pixel-level annotations triggered the advent of unsupervised semantic segmentation. Although the recent work employing the vision transformer (ViT) backbone shows exceptional performance, there is still a lack of consideration for task-specific training guidance and local semantic ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Seong_Leveraging_Hidden_Positives_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.15014 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Seong_Leveraging_Hidden_Positives_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Seong_Leveraging_Hidden_Positives_for_Unsupervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Backdoor Defense via Deconfounded Representation Learning | Zaixi Zhang, Qi Liu, Zhicai Wang, Zepu Lu, Qingyong Hu | Deep neural networks (DNNs) are recently shown to be vulnerable to backdoor attacks, where attackers embed hidden backdoors in the DNN model by injecting a few poisoned examples into the training dataset. While extensive efforts have been made to detect and remove backdoors from backdoored DNNs, it is still not clear w... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Backdoor_Defense_via_Deconfounded_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Backdoor_Defense_via_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06818 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Backdoor_Defense_via_Deconfounded_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Backdoor_Defense_via_Deconfounded_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
LG-BPN: Local and Global Blind-Patch Network for Self-Supervised Real-World Denoising | Zichun Wang, Ying Fu, Ji Liu, Yulun Zhang | Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blind-spot networks (BSNs). For recent methods targeting real-world denoising, they either suf... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_LG-BPN_Local_and_Global_Blind-Patch_Network_for_Self-Supervised_Real-World_Denoising_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LG-BPN_Local_and_Global_Blind-Patch_Network_for_Self-Supervised_Real-World_Denoising_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_LG-BPN_Local_and_Global_Blind-Patch_Network_for_Self-Supervised_Real-World_Denoising_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient View Synthesis and 3D-Based Multi-Frame Denoising With Multiplane Feature Representations | Thomas Tanay, Aleš Leonardis, Matteo Maggioni | While current multi-frame restoration methods combine information from multiple input images using 2D alignment techniques, recent advances in novel view synthesis are paving the way for a new paradigm relying on volumetric scene representations. In this work, we introduce the first 3D-based multi-frame denoising metho... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tanay_Efficient_View_Synthesis_and_3D-Based_Multi-Frame_Denoising_With_Multiplane_Feature_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tanay_Efficient_View_Synthesis_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18139 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tanay_Efficient_View_Synthesis_and_3D-Based_Multi-Frame_Denoising_With_Multiplane_Feature_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tanay_Efficient_View_Synthesis_and_3D-Based_Multi-Frame_Denoising_With_Multiplane_Feature_CVPR_2023_paper.html | CVPR 2023 | null |
An Actor-Centric Causality Graph for Asynchronous Temporal Inference in Group Activity | Zhao Xie, Tian Gao, Kewei Wu, Jiao Chang | The causality relation modeling remains a challenging task for group activity recognition. The causality relations describe the influence of some actors (cause actors) on other actors (effect actors). Most existing graph models focus on learning the actor relation with synchronous temporal features, which is insufficie... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xie_An_Actor-Centric_Causality_Graph_for_Asynchronous_Temporal_Inference_in_Group_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xie_An_Actor-Centric_Causality_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_An_Actor-Centric_Causality_Graph_for_Asynchronous_Temporal_Inference_in_Group_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xie_An_Actor-Centric_Causality_Graph_for_Asynchronous_Temporal_Inference_in_Group_CVPR_2023_paper.html | CVPR 2023 | null |
Color Backdoor: A Robust Poisoning Attack in Color Space | Wenbo Jiang, Hongwei Li, Guowen Xu, Tianwei Zhang | Backdoor attacks against neural networks have been intensively investigated, where the adversary compromises the integrity of the victim model, causing it to make wrong predictions for inference samples containing a specific trigger. To make the trigger more imperceptible and human-unnoticeable, a variety of stealthy b... | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Color_Backdoor_A_Robust_Poisoning_Attack_in_Color_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_Color_Backdoor_A_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Color_Backdoor_A_Robust_Poisoning_Attack_in_Color_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Color_Backdoor_A_Robust_Poisoning_Attack_in_Color_Space_CVPR_2023_paper.html | CVPR 2023 | null |
HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling | Yujian Zheng, Zirong Jin, Moran Li, Haibin Huang, Chongyang Ma, Shuguang Cui, Xiaoguang Han | In this work, we tackle the challenging problem of learning-based single-view 3D hair modeling. Due to the great difficulty of collecting paired real image and 3D hair data, using synthetic data to provide prior knowledge for real domain becomes a leading solution. This unfortunately introduces the challenge of domain ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_HairStep_Transfer_Synthetic_to_Real_Using_Strand_and_Depth_Maps_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_HairStep_Transfer_Synthetic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02700 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_HairStep_Transfer_Synthetic_to_Real_Using_Strand_and_Depth_Maps_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_HairStep_Transfer_Synthetic_to_Real_Using_Strand_and_Depth_Maps_CVPR_2023_paper.html | CVPR 2023 | null |
MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences | Yingwei Li, Charles R. Qi, Yin Zhou, Chenxi Liu, Dragomir Anguelov | Occluded and long-range objects are ubiquitous and challenging for 3D object detection. Point cloud sequence data provide unique opportunities to improve such cases, as an occluded or distant object can be observed from different viewpoints or gets better visibility over time. However, the efficiency and effectiveness ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_MoDAR_Using_Motion_Forecasting_for_3D_Object_Detection_in_Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_MoDAR_Using_Motion_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MoDAR_Using_Motion_Forecasting_for_3D_Object_Detection_in_Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MoDAR_Using_Motion_Forecasting_for_3D_Object_Detection_in_Point_CVPR_2023_paper.html | CVPR 2023 | null |
How You Feelin'? Learning Emotions and Mental States in Movie Scenes | Dhruv Srivastava, Aditya Kumar Singh, Makarand Tapaswi | Movie story analysis requires understanding characters' emotions and mental states. Towards this goal, we formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene and for each character. We propose EmoTx, a multimodal Transformer-based architecture that inges... | https://openaccess.thecvf.com/content/CVPR2023/papers/Srivastava_How_You_Feelin_Learning_Emotions_and_Mental_States_in_Movie_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Srivastava_How_You_Feelin_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05634 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Srivastava_How_You_Feelin_Learning_Emotions_and_Mental_States_in_Movie_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Srivastava_How_You_Feelin_Learning_Emotions_and_Mental_States_in_Movie_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Inference With Grounding Based Vision and Language Models | Burak Uzkent, Amanmeet Garg, Wentao Zhu, Keval Doshi, Jingru Yi, Xiaolong Wang, Mohamed Omar | Transformers have been recently utilized for vision and language tasks successfully. For example, recent image and language models with more than 200M parameters have been proposed to learn visual grounding in the pre-training step and show impressive results on downstream vision and language tasks. On the other hand, ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Uzkent_Dynamic_Inference_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Uzkent_Dynamic_Inference_With_Grounding_Based_Vision_and_Language_Models_CVPR_2023_paper.html | CVPR 2023 | null |
ALSO: Automotive Lidar Self-Supervision by Occupancy Estimation | Alexandre Boulch, Corentin Sautier, Björn Michele, Gilles Puy, Renaud Marlet | We propose a new self-supervised method for pre-training the backbone of deep perception models operating on point clouds. The core idea is to train the model on a pretext task which is the reconstruction of the surface on which the 3D points are sampled, and to use the underlying latent vectors as input to the percept... | https://openaccess.thecvf.com/content/CVPR2023/papers/Boulch_ALSO_Automotive_Lidar_Self-Supervision_by_Occupancy_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Boulch_ALSO_Automotive_Lidar_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.05867 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Boulch_ALSO_Automotive_Lidar_Self-Supervision_by_Occupancy_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Boulch_ALSO_Automotive_Lidar_Self-Supervision_by_Occupancy_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
Connecting Vision and Language With Video Localized Narratives | Paul Voigtlaender, Soravit Changpinyo, Jordi Pont-Tuset, Radu Soricut, Vittorio Ferrari | We propose Video Localized Narratives, a new form of multimodal video annotations connecting vision and language. In the original Localized Narratives, annotators speak and move their mouse simultaneously on an image, thus grounding each word with a mouse trace segment. However, this is challenging on a video. Our new ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Voigtlaender_Connecting_Vision_and_Language_With_Video_Localized_Narratives_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Voigtlaender_Connecting_Vision_and_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2302.11217 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Voigtlaender_Connecting_Vision_and_Language_With_Video_Localized_Narratives_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Voigtlaender_Connecting_Vision_and_Language_With_Video_Localized_Narratives_CVPR_2023_paper.html | CVPR 2023 | null |
Diverse Embedding Expansion Network and Low-Light Cross-Modality Benchmark for Visible-Infrared Person Re-Identification | Yukang Zhang, Hanzi Wang | For the visible-infrared person re-identification (VIReID) task, one of the major challenges is the modality gaps between visible (VIS) and infrared (IR) images. However, the training samples are usually limited, while the modality gaps are too large, which leads that the existing methods cannot effectively mine divers... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.14481 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Diverse_Embedding_Expansion_Network_and_Low-Light_Cross-Modality_Benchmark_for_Visible-Infrared_CVPR_2023_paper.html | CVPR 2023 | null |
Model Barrier: A Compact Un-Transferable Isolation Domain for Model Intellectual Property Protection | Lianyu Wang, Meng Wang, Daoqiang Zhang, Huazhu Fu | As the scientific and technological achievements produced by human intellectual labor and computation cost, model intellectual property (IP) protection, which refers to preventing the usage of the well-trained model on an unauthorized domain, deserves further attention, so as to effectively mobilize the enthusiasm of m... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Model_Barrier_A_Compact_Un-Transferable_Isolation_Domain_for_Model_Intellectual_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.11078 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Model_Barrier_A_Compact_Un-Transferable_Isolation_Domain_for_Model_Intellectual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Model_Barrier_A_Compact_Un-Transferable_Isolation_Domain_for_Model_Intellectual_CVPR_2023_paper.html | CVPR 2023 | null |
Object Detection With Self-Supervised Scene Adaptation | Zekun Zhang, Minh Hoai | This paper proposes a novel method to improve the performance of a trained object detector on scenes with fixed camera perspectives based on self-supervised adaptation. Given a specific scene, the trained detector is adapted using pseudo-ground truth labels generated by the detector itself and an object tracker in a cr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Object_Detection_With_Self-Supervised_Scene_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Object_Detection_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Object_Detection_With_Self-Supervised_Scene_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Object_Detection_With_Self-Supervised_Scene_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Visual-Language Prompt Tuning With Knowledge-Guided Context Optimization | Hantao Yao, Rui Zhang, Changsheng Xu | Prompt tuning is an effective way to adapt the pretrained visual-language model (VLM) to the downstream task using task-related textual tokens. Representative CoOp-based works combine the learnable textual tokens with the class tokens to obtain specific textual knowledge. However, the specific textual knowledge has wor... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Visual-Language_Prompt_Tuning_With_Knowledge-Guided_Context_Optimization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_Visual-Language_Prompt_Tuning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13283 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Visual-Language_Prompt_Tuning_With_Knowledge-Guided_Context_Optimization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Visual-Language_Prompt_Tuning_With_Knowledge-Guided_Context_Optimization_CVPR_2023_paper.html | CVPR 2023 | null |
Weakly Supervised Video Representation Learning With Unaligned Text for Sequential Videos | Sixun Dong, Huazhang Hu, Dongze Lian, Weixin Luo, Yicheng Qian, Shenghua Gao | Sequential video understanding, as an emerging video understanding task, has driven lots of researchers' attention because of its goal-oriented nature. This paper studies weakly supervised sequential video understanding where the accurate time-stamp level text-video alignment is not provided. We solve this task by borr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Dong_Weakly_Supervised_Video_Representation_Learning_With_Unaligned_Text_for_Sequential_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dong_Weakly_Supervised_Video_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12370 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Weakly_Supervised_Video_Representation_Learning_With_Unaligned_Text_for_Sequential_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dong_Weakly_Supervised_Video_Representation_Learning_With_Unaligned_Text_for_Sequential_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Positioning Point-Based Transformer for Point Cloud Understanding | Jinyoung Park, Sanghyeok Lee, Sihyeon Kim, Yunyang Xiong, Hyunwoo J. Kim | Transformers have shown superior performance on various computer vision tasks with their capabilities to capture long-range dependencies. Despite the success, it is challenging to directly apply Transformers on point clouds due to their quadratic cost in the number of points. In this paper, we present a Self-Positionin... | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_Self-Positioning_Point-Based_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16450 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_Self-Positioning_Point-Based_Transformer_for_Point_Cloud_Understanding_CVPR_2023_paper.html | CVPR 2023 | null |
Bootstrap Your Own Prior: Towards Distribution-Agnostic Novel Class Discovery | Muli Yang, Liancheng Wang, Cheng Deng, Hanwang Zhang | Novel Class Discovery (NCD) aims to discover unknown classes without any annotation, by exploiting the transferable knowledge already learned from a base set of known classes. Existing works hold an impractical assumption that the novel class distribution prior is uniform, yet neglect the imbalanced nature of real-worl... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Bootstrap_Your_Own_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Bootstrap_Your_Own_Prior_Towards_Distribution-Agnostic_Novel_Class_Discovery_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Generate Image Embeddings With User-Level Differential Privacy | Zheng Xu, Maxwell Collins, Yuxiao Wang, Liviu Panait, Sewoong Oh, Sean Augenstein, Ting Liu, Florian Schroff, H. Brendan McMahan | Small on-device models have been successfully trained with user-level differential privacy (DP) for next word prediction and image classification tasks in the past. However, existing methods can fail when directly applied to learn embedding models using supervised training data with a large class space. To achieve user... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_To_Generate_Image_Embeddings_With_User-Level_Differential_Privacy_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Learning_To_Generate_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.10844 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_To_Generate_Image_Embeddings_With_User-Level_Differential_Privacy_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_To_Generate_Image_Embeddings_With_User-Level_Differential_Privacy_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Vocabulary Panoptic Segmentation With Text-to-Image Diffusion Models | Jiarui Xu, Sifei Liu, Arash Vahdat, Wonmin Byeon, Xiaolong Wang, Shalini De Mello | We present ODISE: Open-vocabulary DIffusion-based panoptic SEgmentation, which unifies pre-trained text-image diffusion and discriminative models to perform open-vocabulary panoptic segmentation. Text-to-image diffusion models have the remarkable ability to generate high-quality images with diverse open-vocabulary lang... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Open-Vocabulary_Panoptic_Segmentation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.04803 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Open-Vocabulary_Panoptic_Segmentation_With_Text-to-Image_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Open-Vocabulary Semantic Segmentation Models From Natural Language Supervision | Jilan Xu, Junlin Hou, Yuejie Zhang, Rui Feng, Yi Wang, Yu Qiao, Weidi Xie | In this paper, we consider the problem of open-vocabulary semantic segmentation (OVS), which aims to segment objects of arbitrary classes instead of pre-defined, closed-set categories. The main contributions are as follows: First, we propose a transformer-based model for OVS, termed as OVSegmentor, which only exploits ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_Open-Vocabulary_Semantic_Segmentation_Models_From_Natural_Language_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Learning_Open-Vocabulary_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.09121 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Open-Vocabulary_Semantic_Segmentation_Models_From_Natural_Language_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Open-Vocabulary_Semantic_Segmentation_Models_From_Natural_Language_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Dynamic Style Kernels for Artistic Style Transfer | Wenju Xu, Chengjiang Long, Yongwei Nie | Arbitrary style transfer has been demonstrated to be efficient in artistic image generation. Previous methods either globally modulate the content feature ignoring local details, or overly focus on the local structure details leading to style leakage. In contrast to the literature, we propose a new scheme "style kernel... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Learning_Dynamic_Style_Kernels_for_Artistic_Style_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Learning_Dynamic_Style_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00414 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Dynamic_Style_Kernels_for_Artistic_Style_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Learning_Dynamic_Style_Kernels_for_Artistic_Style_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
DeepLSD: Line Segment Detection and Refinement With Deep Image Gradients | Rémi Pautrat, Daniel Barath, Viktor Larsson, Martin R. Oswald, Marc Pollefeys | Line segments are ubiquitous in our human-made world and are increasingly used in vision tasks. They are complementary to feature points thanks to their spatial extent and the structural information they provide. Traditional line detectors based on the image gradient are extremely fast and accurate, but lack robustness... | https://openaccess.thecvf.com/content/CVPR2023/papers/Pautrat_DeepLSD_Line_Segment_Detection_and_Refinement_With_Deep_Image_Gradients_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pautrat_DeepLSD_Line_Segment_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.07766 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pautrat_DeepLSD_Line_Segment_Detection_and_Refinement_With_Deep_Image_Gradients_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pautrat_DeepLSD_Line_Segment_Detection_and_Refinement_With_Deep_Image_Gradients_CVPR_2023_paper.html | CVPR 2023 | null |
OcTr: Octree-Based Transformer for 3D Object Detection | Chao Zhou, Yanan Zhang, Jiaxin Chen, Di Huang | A key challenge for LiDAR-based 3D object detection is to capture sufficient features from large scale 3D scenes especially for distant or/and occluded objects. Albeit recent efforts made by Transformers with the long sequence modeling capability, they fail to properly balance the accuracy and efficiency, suffering fro... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_OcTr_Octree-Based_Transformer_for_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_OcTr_Octree-Based_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12621 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_OcTr_Octree-Based_Transformer_for_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_OcTr_Octree-Based_Transformer_for_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Chat2Map: Efficient Scene Mapping From Multi-Ego Conversations | Sagnik Majumder, Hao Jiang, Pierre Moulon, Ethan Henderson, Paul Calamia, Kristen Grauman, Vamsi Krishna Ithapu | Can conversational videos captured from multiple egocentric viewpoints reveal the map of a scene in a cost-efficient way? We seek to answer this question by proposing a new problem: efficiently building the map of a previously unseen 3D environment by exploiting shared information in the egocentric audio-visual observa... | https://openaccess.thecvf.com/content/CVPR2023/papers/Majumder_Chat2Map_Efficient_Scene_Mapping_From_Multi-Ego_Conversations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Majumder_Chat2Map_Efficient_Scene_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.02184 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Majumder_Chat2Map_Efficient_Scene_Mapping_From_Multi-Ego_Conversations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Majumder_Chat2Map_Efficient_Scene_Mapping_From_Multi-Ego_Conversations_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Distortion Invariant Representation for Image Restoration From a Causality Perspective | Xin Li, Bingchen Li, Xin Jin, Cuiling Lan, Zhibo Chen | In recent years, we have witnessed the great advancement of Deep neural networks (DNNs) in image restoration. However, a critical limitation is that they cannot generalize well to real-world degradations with different degrees or types. In this paper, we are the first to propose a novel training strategy for image rest... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Learning_Distortion_Invariant_Representation_for_Image_Restoration_From_a_Causality_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Learning_Distortion_Invariant_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06859 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Distortion_Invariant_Representation_for_Image_Restoration_From_a_Causality_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Learning_Distortion_Invariant_Representation_for_Image_Restoration_From_a_Causality_CVPR_2023_paper.html | CVPR 2023 | null |
MOT: Masked Optimal Transport for Partial Domain Adaptation | You-Wei Luo, Chuan-Xian Ren | As an important methodology to measure distribution discrepancy, optimal transport (OT) has been successfully applied to learn generalizable visual models under changing environments. However, there are still limitations, including strict prior assumption and implicit alignment, for current OT modeling in challenging r... | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_MOT_Masked_Optimal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_MOT_Masked_Optimal_Transport_for_Partial_Domain_Adaptation_CVPR_2023_paper.html | CVPR 2023 | null |
Executing Your Commands via Motion Diffusion in Latent Space | Xin Chen, Biao Jiang, Wen Liu, Zilong Huang, Bin Fu, Tao Chen, Gang Yu | We study a challenging task, conditional human motion generation, which produces plausible human motion sequences according to various conditional inputs, such as action classes or textual descriptors. Since human motions are highly diverse and have a property of quite different distribution from conditional modalities... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Executing_Your_Commands_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04048 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Executing_Your_Commands_via_Motion_Diffusion_in_Latent_Space_CVPR_2023_paper.html | CVPR 2023 | null |
GeoMAE: Masked Geometric Target Prediction for Self-Supervised Point Cloud Pre-Training | Xiaoyu Tian, Haoxi Ran, Yue Wang, Hang Zhao | This paper tries to address a fundamental question in point cloud self-supervised learning: what is a good signal we should leverage to learn features from point clouds without annotations? To answer that, we introduce a point cloud representation learning framework, based on geometric feature reconstruction. In contra... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_GeoMAE_Masked_Geometric_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.08808 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_GeoMAE_Masked_Geometric_Target_Prediction_for_Self-Supervised_Point_Cloud_Pre-Training_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Conditional Attributes for Compositional Zero-Shot Learning | Qingsheng Wang, Lingqiao Liu, Chenchen Jing, Hao Chen, Guoqiang Liang, Peng Wang, Chunhua Shen | Compositional Zero-Shot Learning (CZSL) aims to train models to recognize novel compositional concepts based on learned concepts such as attribute-object combinations. One of the challenges is to model attributes interacted with different objects, e.g., the attribute "wet" in "wet apple" and "wet cat" is different. As ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Learning_Conditional_Attributes_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_Conditional_Attributes_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Learning_Conditional_Attributes_for_Compositional_Zero-Shot_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Complete 3D Human Reconstruction From a Single Incomplete Image | Junying Wang, Jae Shin Yoon, Tuanfeng Y. Wang, Krishna Kumar Singh, Ulrich Neumann | This paper presents a method to reconstruct a complete human geometry and texture from an image of a person with only partial body observed, e.g., a torso. The core challenge arises from the occlusion: there exists no pixel to reconstruct where many existing single-view human reconstruction methods are not designed to ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Complete_3D_Human_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Complete_3D_Human_Reconstruction_From_a_Single_Incomplete_Image_CVPR_2023_paper.html | CVPR 2023 | null |
PVT-SSD: Single-Stage 3D Object Detector With Point-Voxel Transformer | Honghui Yang, Wenxiao Wang, Minghao Chen, Binbin Lin, Tong He, Hua Chen, Xiaofei He, Wanli Ouyang | Recent Transformer-based 3D object detectors learn point cloud features either from point- or voxel-based representations. However, the former requires time-consuming sampling while the latter introduces quantization errors. In this paper, we present a novel Point-Voxel Transformer for single-stage 3D detection (PVT-SS... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_PVT-SSD_Single-Stage_3D_Object_Detector_With_Point-Voxel_Transformer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_PVT-SSD_Single-Stage_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_PVT-SSD_Single-Stage_3D_Object_Detector_With_Point-Voxel_Transformer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_PVT-SSD_Single-Stage_3D_Object_Detector_With_Point-Voxel_Transformer_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Human Matting for Dynamic Videos | Chung-Ching Lin, Jiang Wang, Kun Luo, Kevin Lin, Linjie Li, Lijuan Wang, Zicheng Liu | The most recent efforts in video matting have focused on eliminating trimap dependency since trimap annotations are expensive and trimap-based methods are less adaptable for real-time applications. Despite the latest tripmap-free methods showing promising results, their performance often degrades when dealing with high... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Adaptive_Human_Matting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06018 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Adaptive_Human_Matting_for_Dynamic_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Common Rationale To Improve Self-Supervised Representation for Fine-Grained Visual Recognition Problems | Yangyang Shu, Anton van den Hengel, Lingqiao Liu | Self-supervised learning (SSL) strategies have demonstrated remarkable performance in various recognition tasks. However, both our preliminary investigation and recent studies suggest that they may be less effective in learning representations for fine-grained visual recognition (FGVR) since many features helpful for o... | https://openaccess.thecvf.com/content/CVPR2023/papers/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shu_Learning_Common_Rationale_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01669 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shu_Learning_Common_Rationale_To_Improve_Self-Supervised_Representation_for_Fine-Grained_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
Reconstructing Animatable Categories From Videos | Gengshan Yang, Chaoyang Wang, N. Dinesh Reddy, Deva Ramanan | Building animatable 3D models is challenging due to the need for 3D scans, laborious registration, and manual rigging. Recently, differentiable rendering provides a pathway to obtain high-quality 3D models from monocular videos, but these are limited to rigid categories or single instances. We present RAC, a method to ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Reconstructing_Animatable_Categories_From_Videos_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Reconstructing_Animatable_Categories_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.06351 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Reconstructing_Animatable_Categories_From_Videos_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Reconstructing_Animatable_Categories_From_Videos_CVPR_2023_paper.html | CVPR 2023 | null |
UDE: A Unified Driving Engine for Human Motion Generation | Zixiang Zhou, Baoyuan Wang | Generating controllable and editable human motion sequences is a key challenge in 3D Avatar generation. It has been labor-intensive to generate and animate human motion for a long time until learning-based approaches have been developed and applied recently. However, these approaches are still task-specific or modality... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_UDE_A_Unified_Driving_Engine_for_Human_Motion_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_UDE_A_Unified_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16016 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_UDE_A_Unified_Driving_Engine_for_Human_Motion_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_UDE_A_Unified_Driving_Engine_for_Human_Motion_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
High-Fidelity 3D Human Digitization From Single 2K Resolution Images | Sang-Hun Han, Min-Gyu Park, Ju Hong Yoon, Ju-Mi Kang, Young-Jae Park, Hae-Gon Jeon | High-quality 3D human body reconstruction requires high-fidelity and large-scale training data and appropriate network design that effectively exploits the high-resolution input images. To tackle these problems, we propose a simple yet effective 3D human digitization method called 2K2K, which constructs a large-scale 2... | https://openaccess.thecvf.com/content/CVPR2023/papers/Han_High-Fidelity_3D_Human_Digitization_From_Single_2K_Resolution_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Han_High-Fidelity_3D_Human_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15108 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Han_High-Fidelity_3D_Human_Digitization_From_Single_2K_Resolution_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Han_High-Fidelity_3D_Human_Digitization_From_Single_2K_Resolution_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Co-Salient Object Detection With Uncertainty-Aware Group Exchange-Masking | Yang Wu, Huihui Song, Bo Liu, Kaihua Zhang, Dong Liu | The traditional definition of co-salient object detection (CoSOD) task is to segment the common salient objects in a group of relevant images. Existing CoSOD models by default adopt the group consensus assumption. This brings about model robustness defect under the condition of irrelevant images in the testing image gr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Co-Salient_Object_Detection_With_Uncertainty-Aware_Group_Exchange-Masking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Co-Salient_Object_Detection_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Co-Salient_Object_Detection_With_Uncertainty-Aware_Group_Exchange-Masking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Co-Salient_Object_Detection_With_Uncertainty-Aware_Group_Exchange-Masking_CVPR_2023_paper.html | CVPR 2023 | null |
Tangentially Elongated Gaussian Belief Propagation for Event-Based Incremental Optical Flow Estimation | Jun Nagata, Yusuke Sekikawa | Optical flow estimation is a fundamental functionality in computer vision. An event-based camera, which asynchronously detects sparse intensity changes, is an ideal device for realizing low-latency estimation of the optical flow owing to its low-latency sensing mechanism. An existing method using local plane fitting of... | https://openaccess.thecvf.com/content/CVPR2023/papers/Nagata_Tangentially_Elongated_Gaussian_Belief_Propagation_for_Event-Based_Incremental_Optical_Flow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nagata_Tangentially_Elongated_Gaussian_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nagata_Tangentially_Elongated_Gaussian_Belief_Propagation_for_Event-Based_Incremental_Optical_Flow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nagata_Tangentially_Elongated_Gaussian_Belief_Propagation_for_Event-Based_Incremental_Optical_Flow_CVPR_2023_paper.html | CVPR 2023 | null |
Extracting Class Activation Maps From Non-Discriminative Features As Well | Zhaozheng Chen, Qianru Sun | Extracting class activation maps (CAM) from a classification model often results in poor coverage on foreground objects, i.e., only the discriminative region (e.g., the "head" of "sheep") is recognized and the rest (e.g., the "leg" of "sheep") mistakenly as background. The crux behind is that the weight of the classifi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_Extracting_Class_Activation_Maps_From_Non-Discriminative_Features_As_Well_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_Extracting_Class_Activation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10334 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Extracting_Class_Activation_Maps_From_Non-Discriminative_Features_As_Well_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_Extracting_Class_Activation_Maps_From_Non-Discriminative_Features_As_Well_CVPR_2023_paper.html | CVPR 2023 | null |
BlendFields: Few-Shot Example-Driven Facial Modeling | Kacper Kania, Stephan J. Garbin, Andrea Tagliasacchi, Virginia Estellers, Kwang Moo Yi, Julien Valentin, Tomasz Trzciński, Marek Kowalski | Generating faithful visualizations of human faces requires capturing both coarse and fine-level details of the face geometry and appearance. Existing methods are either data-driven, requiring an extensive corpus of data not publicly accessible to the research community, or fail to capture fine details because they rely... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kania_BlendFields_Few-Shot_Example-Driven_Facial_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kania_BlendFields_Few-Shot_Example-Driven_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2305.07514 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kania_BlendFields_Few-Shot_Example-Driven_Facial_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kania_BlendFields_Few-Shot_Example-Driven_Facial_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Sparse Pairwise Loss for Object Re-Identification | Xiao Zhou, Yujie Zhong, Zhen Cheng, Fan Liang, Lin Ma | Object re-identification (ReID) aims to find instances with the same identity as the given probe from a large gallery. Pairwise losses play an important role in training a strong ReID network. Existing pairwise losses densely exploit each instance as an anchor and sample its triplets in a mini-batch. This dense samplin... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Adaptive_Sparse_Pairwise_Loss_for_Object_Re-Identification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Adaptive_Sparse_Pairwise_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.18247 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Adaptive_Sparse_Pairwise_Loss_for_Object_Re-Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Adaptive_Sparse_Pairwise_Loss_for_Object_Re-Identification_CVPR_2023_paper.html | CVPR 2023 | null |
NeFII: Inverse Rendering for Reflectance Decomposition With Near-Field Indirect Illumination | Haoqian Wu, Zhipeng Hu, Lincheng Li, Yongqiang Zhang, Changjie Fan, Xin Yu | Inverse rendering methods aim to estimate geometry, materials and illumination from multi-view RGB images. In order to achieve better decomposition, recent approaches attempt to model indirect illuminations reflected from different materials via Spherical Gaussians (SG), which, however, tends to blur the high-frequency... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_NeFII_Inverse_Rendering_for_Reflectance_Decomposition_With_Near-Field_Indirect_Illumination_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_NeFII_Inverse_Rendering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16617 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_NeFII_Inverse_Rendering_for_Reflectance_Decomposition_With_Near-Field_Indirect_Illumination_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_NeFII_Inverse_Rendering_for_Reflectance_Decomposition_With_Near-Field_Indirect_Illumination_CVPR_2023_paper.html | CVPR 2023 | null |
Towards Professional Level Crowd Annotation of Expert Domain Data | Pei Wang, Nuno Vasconcelos | Image recognition on expert domains is usually fine-grained and requires expert labeling, which is costly. This limits dataset sizes and the accuracy of learning systems. To address this challenge, we consider annotating expert data with crowdsourcing. This is denoted as PrOfeSsional lEvel cRowd (POSER) annotation. A n... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Towards_Professional_Level_Crowd_Annotation_of_Expert_Domain_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Towards_Professional_Level_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Towards_Professional_Level_Crowd_Annotation_of_Expert_Domain_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Towards_Professional_Level_Crowd_Annotation_of_Expert_Domain_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Fully Self-Supervised Depth Estimation From Defocus Clue | Haozhe Si, Bin Zhao, Dong Wang, Yunpeng Gao, Mulin Chen, Zhigang Wang, Xuelong Li | Depth-from-defocus (DFD), modeling the relationship between depth and defocus pattern in images, has demonstrated promising performance in depth estimation. Recently, several self-supervised works try to overcome the difficulties in acquiring accurate depth ground-truth. However, they depend on the all-in-focus (AIF) i... | https://openaccess.thecvf.com/content/CVPR2023/papers/Si_Fully_Self-Supervised_Depth_Estimation_From_Defocus_Clue_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Si_Fully_Self-Supervised_Depth_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10752 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Si_Fully_Self-Supervised_Depth_Estimation_From_Defocus_Clue_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Si_Fully_Self-Supervised_Depth_Estimation_From_Defocus_Clue_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Weakly Supervised Object Kinematic Motion Prediction | Gengxin Liu, Qian Sun, Haibin Huang, Chongyang Ma, Yulan Guo, Li Yi, Hui Huang, Ruizhen Hu | Given a 3D object, kinematic motion prediction aims to identify the mobile parts as well as the corresponding motion parameters. Due to the large variations in both topological structure and geometric details of 3D objects, this remains a challenging task and the lack of large scale labeled data also constrain the perf... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Semi-Weakly_Supervised_Object_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.17774 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Semi-Weakly_Supervised_Object_Kinematic_Motion_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Learning a Simple Low-Light Image Enhancer From Paired Low-Light Instances | Zhenqi Fu, Yan Yang, Xiaotong Tu, Yue Huang, Xinghao Ding, Kai-Kuang Ma | Low-light Image Enhancement (LIE) aims at improving contrast and restoring details for images captured in low-light conditions. Most of the previous LIE algorithms adjust illumination using a single input image with several handcrafted priors. Those solutions, however, often fail in revealing image details due to the l... | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_Learning_a_Simple_Low-Light_Image_Enhancer_From_Paired_Low-Light_Instances_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Learning_a_Simple_Low-Light_Image_Enhancer_From_Paired_Low-Light_Instances_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_Learning_a_Simple_Low-Light_Image_Enhancer_From_Paired_Low-Light_Instances_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Stereo Video Inpainting | Zhiliang Wu, Changchang Sun, Hanyu Xuan, Yan Yan | Stereo video inpainting aims to fill the missing regions on the left and right views of the stereo video with plausible content simultaneously. Compared with the single video inpainting that has achieved promising results using deep convolutional neural networks, inpainting the missing regions of stereo video has not b... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Deep_Stereo_Video_Inpainting_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Deep_Stereo_Video_Inpainting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Deep_Stereo_Video_Inpainting_CVPR_2023_paper.html | CVPR 2023 | null |
Prompting Large Language Models With Answer Heuristics for Knowledge-Based Visual Question Answering | Zhenwei Shao, Zhou Yu, Meng Wang, Jun Yu | Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent wor... | https://openaccess.thecvf.com/content/CVPR2023/papers/Shao_Prompting_Large_Language_Models_With_Answer_Heuristics_for_Knowledge-Based_Visual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shao_Prompting_Large_Language_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01903 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_Prompting_Large_Language_Models_With_Answer_Heuristics_for_Knowledge-Based_Visual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shao_Prompting_Large_Language_Models_With_Answer_Heuristics_for_Knowledge-Based_Visual_CVPR_2023_paper.html | CVPR 2023 | null |
IFSeg: Image-Free Semantic Segmentation via Vision-Language Model | Sukmin Yun, Seong Hyeon Park, Paul Hongsuck Seo, Jinwoo Shin | Vision-language (VL) pre-training has recently gained much attention for its transferability and flexibility in novel concepts (e.g., cross-modality transfer) across various visual tasks. However, VL-driven segmentation has been under-explored, and the existing approaches still have the burden of acquiring additional t... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yun_IFSeg_Image-Free_Semantic_Segmentation_via_Vision-Language_Model_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yun_IFSeg_Image-Free_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14396 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yun_IFSeg_Image-Free_Semantic_Segmentation_via_Vision-Language_Model_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yun_IFSeg_Image-Free_Semantic_Segmentation_via_Vision-Language_Model_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Robustness of Semantic Segmentation to Motion-Blur Using Class-Centric Augmentation | Aakanksha, A. N. Rajagopalan | Semantic segmentation involves classifying each pixel into one of a pre-defined set of object/stuff classes. Such a fine-grained detection and localization of objects in the scene is challenging by itself. The complexity increases manifold in the presence of blur. With cameras becoming increasingly light-weight and com... | https://openaccess.thecvf.com/content/CVPR2023/papers/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Aakanksha_Improving_Robustness_of_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Aakanksha_Improving_Robustness_of_Semantic_Segmentation_to_Motion-Blur_Using_Class-Centric_Augmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Progressive Open Space Expansion for Open-Set Model Attribution | Tianyun Yang, Danding Wang, Fan Tang, Xinying Zhao, Juan Cao, Sheng Tang | Despite the remarkable progress in generative technology, the Janus-faced issues of intellectual property protection and malicious content supervision have arisen. Efforts have been paid to manage synthetic images by attributing them to a set of potential source models. However, the closed-set classification setting li... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Progressive_Open_Space_Expansion_for_Open-Set_Model_Attribution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Progressive_Open_Space_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.06877 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Progressive_Open_Space_Expansion_for_Open-Set_Model_Attribution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Progressive_Open_Space_Expansion_for_Open-Set_Model_Attribution_CVPR_2023_paper.html | CVPR 2023 | null |
Backdoor Cleansing With Unlabeled Data | Lu Pang, Tao Sun, Haibin Ling, Chao Chen | Due to the increasing computational demand of Deep Neural Networks (DNNs), companies and organizations have begun to outsource the training process. However, the externally trained DNNs can potentially be backdoor attacked. It is crucial to defend against such attacks, i.e, to postprocess a suspicious model so that its... | https://openaccess.thecvf.com/content/CVPR2023/papers/Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pang_Backdoor_Cleansing_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12044 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pang_Backdoor_Cleansing_With_Unlabeled_Data_CVPR_2023_paper.html | CVPR 2023 | null |
Is BERT Blind? Exploring the Effect of Vision-and-Language Pretraining on Visual Language Understanding | Morris Alper, Michael Fiman, Hadar Averbuch-Elor | Most humans use visual imagination to understand and reason about language, but models such as BERT reason about language using knowledge acquired during text-only pretraining. In this work, we investigate whether vision-and-language pretraining can improve performance on text-only tasks that involve implicit visual re... | https://openaccess.thecvf.com/content/CVPR2023/papers/Alper_Is_BERT_Blind_Exploring_the_Effect_of_Vision-and-Language_Pretraining_on_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Alper_Is_BERT_Blind_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.12513 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Alper_Is_BERT_Blind_Exploring_the_Effect_of_Vision-and-Language_Pretraining_on_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Alper_Is_BERT_Blind_Exploring_the_Effect_of_Vision-and-Language_Pretraining_on_CVPR_2023_paper.html | CVPR 2023 | null |
PivoTAL: Prior-Driven Supervision for Weakly-Supervised Temporal Action Localization | Mamshad Nayeem Rizve, Gaurav Mittal, Ye Yu, Matthew Hall, Sandra Sajeev, Mubarak Shah, Mei Chen | Weakly-supervised Temporal Action Localization (WTAL) attempts to localize the actions in untrimmed videos using only video-level supervision. Most recent works approach WTAL from a localization-by-classification perspective where these methods try to classify each video frame followed by a manually-designed post-proce... | https://openaccess.thecvf.com/content/CVPR2023/papers/Rizve_PivoTAL_Prior-Driven_Supervision_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rizve_PivoTAL_Prior-Driven_Supervision_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rizve_PivoTAL_Prior-Driven_Supervision_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rizve_PivoTAL_Prior-Driven_Supervision_for_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Harmonious Feature Learning for Interactive Hand-Object Pose Estimation | Zhifeng Lin, Changxing Ding, Huan Yao, Zengsheng Kuang, Shaoli Huang | Joint hand and object pose estimation from a single image is extremely challenging as serious occlusion often occurs when the hand and object interact. Existing approaches typically first extract coarse hand and object features from a single backbone, then further enhance them with reference to each other via interacti... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Harmonious_Feature_Learning_for_Interactive_Hand-Object_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Harmonious_Feature_Learning_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Harmonious_Feature_Learning_for_Interactive_Hand-Object_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Harmonious_Feature_Learning_for_Interactive_Hand-Object_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
3D GAN Inversion With Facial Symmetry Prior | Fei Yin, Yong Zhang, Xuan Wang, Tengfei Wang, Xiaoyu Li, Yuan Gong, Yanbo Fan, Xiaodong Cun, Ying Shan, Cengiz Oztireli, Yujiu Yang | Recently, a surge of high-quality 3D-aware GANs have been proposed, which leverage the generative power of neural rendering. It is natural to associate 3D GANs with GAN inversion methods to project a real image into the generator's latent space, allowing free-view consistent synthesis and editing, referred as 3D GAN in... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_3D_GAN_Inversion_With_Facial_Symmetry_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yin_3D_GAN_Inversion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16927 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_3D_GAN_Inversion_With_Facial_Symmetry_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_3D_GAN_Inversion_With_Facial_Symmetry_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
CLOTH4D: A Dataset for Clothed Human Reconstruction | Xingxing Zou, Xintong Han, Waikeung Wong | Clothed human reconstruction is the cornerstone for creating the virtual world. To a great extent, the quality of recovered avatars decides whether the Metaverse is a passing fad. In this work, we introduce CLOTH4D, a clothed human dataset containing 1,000 subjects with varied appearances, 1,000 3D outfits, and over 10... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zou_CLOTH4D_A_Dataset_for_Clothed_Human_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zou_CLOTH4D_A_Dataset_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zou_CLOTH4D_A_Dataset_for_Clothed_Human_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zou_CLOTH4D_A_Dataset_for_Clothed_Human_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generation | Yen-Chi Cheng, Hsin-Ying Lee, Sergey Tulyakov, Alexander G. Schwing, Liang-Yan Gui | In this work, we present a novel framework built to simplify 3D asset generation for amateur users. To enable interactive generation, our method supports a variety of input modalities that can be easily provided by a human, including images, texts, partially observed shapes and combinations of these, further allowing f... | https://openaccess.thecvf.com/content/CVPR2023/papers/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cheng_SDFusion_Multimodal_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.04493 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cheng_SDFusion_Multimodal_3D_Shape_Completion_Reconstruction_and_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
SMAE: Few-Shot Learning for HDR Deghosting With Saturation-Aware Masked Autoencoders | Qingsen Yan, Song Zhang, Weiye Chen, Hao Tang, Yu Zhu, Jinqiu Sun, Luc Van Gool, Yanning Zhang | Generating a high-quality High Dynamic Range (HDR) image from dynamic scenes has recently been extensively studied by exploiting Deep Neural Networks (DNNs). Most DNNs-based methods require a large amount of training data with ground truth, requiring tedious and time-consuming work. Few-shot HDR imaging aims to generat... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.06914 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_SMAE_Few-Shot_Learning_for_HDR_Deghosting_With_Saturation-Aware_Masked_Autoencoders_CVPR_2023_paper.html | CVPR 2023 | null |
Improving Generalization With Domain Convex Game | Fangrui Lv, Jian Liang, Shuang Li, Jinming Zhang, Di Liu | Domain generalization (DG) tends to alleviate the poor generalization capability of deep neural networks by learning model with multiple source domains. A classical solution to DG is domain augmentation, the common belief of which is that diversifying source domains will be conducive to the out-of-distribution generali... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lv_Improving_Generalization_With_Domain_Convex_Game_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lv_Improving_Generalization_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13297 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lv_Improving_Generalization_With_Domain_Convex_Game_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lv_Improving_Generalization_With_Domain_Convex_Game_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Render Novel Views From Wide-Baseline Stereo Pairs | Yilun Du, Cameron Smith, Ayush Tewari, Vincent Sitzmann | We introduce a method for novel view synthesis given only a single wide-baseline stereo image pair. In this challenging regime, 3D scene points are regularly observed only once, requiring prior-based reconstruction of scene geometry and appearance. We find that existing approaches to novel view synthesis from sparse ob... | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Learning_To_Render_Novel_Views_From_Wide-Baseline_Stereo_Pairs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Learning_To_Render_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2304.08463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Learning_To_Render_Novel_Views_From_Wide-Baseline_Stereo_Pairs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Learning_To_Render_Novel_Views_From_Wide-Baseline_Stereo_Pairs_CVPR_2023_paper.html | CVPR 2023 | null |
TryOnDiffusion: A Tale of Two UNets | Luyang Zhu, Dawei Yang, Tyler Zhu, Fitsum Reda, William Chan, Chitwan Saharia, Mohammad Norouzi, Ira Kemelmacher-Shlizerman | Given two images depicting a person and a garment worn by another person, our goal is to generate a visualization of how the garment might look on the input person. A key challenge is to synthesize a photorealistic detail-preserving visualization of the garment, while warping the garment to accommodate a significant bo... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_TryOnDiffusion_A_Tale_of_Two_UNets_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_TryOnDiffusion_A_Tale_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_TryOnDiffusion_A_Tale_of_Two_UNets_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_TryOnDiffusion_A_Tale_of_Two_UNets_CVPR_2023_paper.html | CVPR 2023 | null |
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