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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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Learning Rotation-Equivariant Features for Visual Correspondence | Jongmin Lee, Byungjin Kim, Seungwook Kim, Minsu Cho | Extracting discriminative local features that are invariant to imaging variations is an integral part of establishing correspondences between images. In this work, we introduce a self-supervised learning framework to extract discriminative rotation-invariant descriptors using group-equivariant CNNs. Thanks to employing... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Learning_Rotation-Equivariant_Features_for_Visual_Correspondence_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Learning_Rotation-Equivariant_Features_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Learning_Rotation-Equivariant_Features_for_Visual_Correspondence_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Learning_Rotation-Equivariant_Features_for_Visual_Correspondence_CVPR_2023_paper.html | CVPR 2023 | null |
DexArt: Benchmarking Generalizable Dexterous Manipulation With Articulated Objects | Chen Bao, Helin Xu, Yuzhe Qin, Xiaolong Wang | To enable general-purpose robots, we will require the robot to operate daily articulated objects as humans do. Current robot manipulation has heavily relied on using a parallel gripper, which restricts the robot to a limited set of objects. On the other hand, operating with a multi-finger robot hand will allow better a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Bao_DexArt_Benchmarking_Generalizable_Dexterous_Manipulation_With_Articulated_Objects_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bao_DexArt_Benchmarking_Generalizable_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2305.05706 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_DexArt_Benchmarking_Generalizable_Dexterous_Manipulation_With_Articulated_Objects_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bao_DexArt_Benchmarking_Generalizable_Dexterous_Manipulation_With_Articulated_Objects_CVPR_2023_paper.html | CVPR 2023 | null |
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly Detection | Xuan Zhang, Shiyu Li, Xi Li, Ping Huang, Jiulong Shan, Ting Chen | Visual anomaly detection, an important problem in computer vision, is usually formulated as a one-class classification and segmentation task. The student-teacher (S-T) framework has proved to be effective in solving this challenge. However, previous works based on S-T only empirically applied constraints on normal data... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_DeSTSeg_Segmentation_Guided_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11317 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_DeSTSeg_Segmentation_Guided_Denoising_Student-Teacher_for_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Rate Estimator and Unsupervised Learning for Efficient Distributed Image Analytics in Split-DNN Models | Nilesh Ahuja, Parual Datta, Bhavya Kanzariya, V. Srinivasa Somayazulu, Omesh Tickoo | Thanks to advances in computer vision and AI, there has been a large growth in the demand for cloud-based visual analytics in which images captured by a low-powered edge device are transmitted to the cloud for analytics. Use of conventional codecs (JPEG, MPEG, HEVC, etc.) for compressing such data introduces artifacts ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ahuja_Neural_Rate_Estimator_and_Unsupervised_Learning_for_Efficient_Distributed_Image_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ahuja_Neural_Rate_Estimator_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ahuja_Neural_Rate_Estimator_and_Unsupervised_Learning_for_Efficient_Distributed_Image_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ahuja_Neural_Rate_Estimator_and_Unsupervised_Learning_for_Efficient_Distributed_Image_CVPR_2023_paper.html | CVPR 2023 | null |
Object Pop-Up: Can We Infer 3D Objects and Their Poses From Human Interactions Alone? | Ilya A. Petrov, Riccardo Marin, Julian Chibane, Gerard Pons-Moll | The intimate entanglement between objects affordances and human poses is of large interest, among others, for behavioural sciences, cognitive psychology, and Computer Vision communities. In recent years, the latter has developed several object-centric approaches: starting from items, learning pipelines synthesizing hum... | https://openaccess.thecvf.com/content/CVPR2023/papers/Petrov_Object_Pop-Up_Can_We_Infer_3D_Objects_and_Their_Poses_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Petrov_Object_Pop-Up_Can_We_Infer_3D_Objects_and_Their_Poses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Petrov_Object_Pop-Up_Can_We_Infer_3D_Objects_and_Their_Poses_CVPR_2023_paper.html | CVPR 2023 | null |
VoP: Text-Video Co-Operative Prompt Tuning for Cross-Modal Retrieval | Siteng Huang, Biao Gong, Yulin Pan, Jianwen Jiang, Yiliang Lv, Yuyuan Li, Donglin Wang | Many recent studies leverage the pre-trained CLIP for text-video cross-modal retrieval by tuning the backbone with additional heavy modules, which not only brings huge computational burdens with much more parameters, but also leads to the knowledge forgetting from upstream models. In this work, we propose the VoP: Text... | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_VoP_Text-Video_Co-Operative_Prompt_Tuning_for_Cross-Modal_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_VoP_Text-Video_Co-Operative_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.12764 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_VoP_Text-Video_Co-Operative_Prompt_Tuning_for_Cross-Modal_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_VoP_Text-Video_Co-Operative_Prompt_Tuning_for_Cross-Modal_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Exploiting Unlabelled Photos for Stronger Fine-Grained SBIR | Aneeshan Sain, Ayan Kumar Bhunia, Subhadeep Koley, Pinaki Nath Chowdhury, Soumitri Chattopadhyay, Tao Xiang, Yi-Zhe Song | This paper advances the fine-grained sketch-based image retrieval (FG-SBIR) literature by putting forward a strong baseline that overshoots prior state-of-the art by 11%. This is not via complicated design though, but by addressing two critical issues facing the community (i) the gold standard triplet loss does not en... | https://openaccess.thecvf.com/content/CVPR2023/papers/Sain_Exploiting_Unlabelled_Photos_for_Stronger_Fine-Grained_SBIR_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sain_Exploiting_Unlabelled_Photos_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13779 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sain_Exploiting_Unlabelled_Photos_for_Stronger_Fine-Grained_SBIR_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sain_Exploiting_Unlabelled_Photos_for_Stronger_Fine-Grained_SBIR_CVPR_2023_paper.html | CVPR 2023 | null |
You Do Not Need Additional Priors or Regularizers in Retinex-Based Low-Light Image Enhancement | Huiyuan Fu, Wenkai Zheng, Xiangyu Meng, Xin Wang, Chuanming Wang, Huadong Ma | Images captured in low-light conditions often suffer from significant quality degradation. Recent works have built a large variety of deep Retinex-based networks to enhance low-light images. The Retinex-based methods require decomposing the image into reflectance and illumination components, which is a highly ill-posed... | https://openaccess.thecvf.com/content/CVPR2023/papers/Fu_You_Do_Not_Need_Additional_Priors_or_Regularizers_in_Retinex-Based_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_You_Do_Not_Need_Additional_Priors_or_Regularizers_in_Retinex-Based_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Fu_You_Do_Not_Need_Additional_Priors_or_Regularizers_in_Retinex-Based_CVPR_2023_paper.html | CVPR 2023 | null |
PIP-Net: Patch-Based Intuitive Prototypes for Interpretable Image Classification | Meike Nauta, Jörg Schlötterer, Maurice van Keulen, Christin Seifert | Interpretable methods based on prototypical patches recognize various components in an image in order to explain their reasoning to humans. However, existing prototype-based methods can learn prototypes that are not in line with human visual perception, i.e., the same prototype can refer to different concepts in the re... | https://openaccess.thecvf.com/content/CVPR2023/papers/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nauta_PIP-Net_Patch-Based_Intuitive_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nauta_PIP-Net_Patch-Based_Intuitive_Prototypes_for_Interpretable_Image_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
SCADE: NeRFs from Space Carving With Ambiguity-Aware Depth Estimates | Mikaela Angelina Uy, Ricardo Martin-Brualla, Leonidas Guibas, Ke Li | Neural radiance fields (NeRFs) have enabled high fidelity 3D reconstruction from multiple 2D input views. However, a well-known drawback of NeRFs is the less-than-ideal performance under a small number of views, due to insufficient constraints enforced by volumetric rendering. To address this issue, we introduce SCADE,... | https://openaccess.thecvf.com/content/CVPR2023/papers/Uy_SCADE_NeRFs_from_Space_Carving_With_Ambiguity-Aware_Depth_Estimates_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Uy_SCADE_NeRFs_from_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13582 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Uy_SCADE_NeRFs_from_Space_Carving_With_Ambiguity-Aware_Depth_Estimates_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Uy_SCADE_NeRFs_from_Space_Carving_With_Ambiguity-Aware_Depth_Estimates_CVPR_2023_paper.html | CVPR 2023 | null |
Re-Thinking Model Inversion Attacks Against Deep Neural Networks | Ngoc-Bao Nguyen, Keshigeyan Chandrasegaran, Milad Abdollahzadeh, Ngai-Man Cheung | Model inversion (MI) attacks aim to infer and reconstruct private training data by abusing access to a model. MI attacks have raised concerns about the leaking of sensitive information (e.g. private face images used in training a face recognition system). Recently, several algorithms for MI have been proposed to improv... | https://openaccess.thecvf.com/content/CVPR2023/papers/Nguyen_Re-Thinking_Model_Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nguyen_Re-Thinking_Model_Inversion_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01669 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Re-Thinking_Model_Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nguyen_Re-Thinking_Model_Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
1% VS 100%: Parameter-Efficient Low Rank Adapter for Dense Predictions | Dongshuo Yin, Yiran Yang, Zhechao Wang, Hongfeng Yu, Kaiwen Wei, Xian Sun | Fine-tuning large-scale pre-trained vision models to downstream tasks is a standard technique for achieving state-of-the-art performance on computer vision benchmarks. However, fine-tuning the whole model with millions of parameters is inefficient as it requires storing a same-sized new model copy for each task. In thi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_1_VS_100_Parameter-Efficient_Low_Rank_Adapter_for_Dense_Predictions_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_1_VS_100_Parameter-Efficient_Low_Rank_Adapter_for_Dense_Predictions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_1_VS_100_Parameter-Efficient_Low_Rank_Adapter_for_Dense_Predictions_CVPR_2023_paper.html | CVPR 2023 | null |
ResFormer: Scaling ViTs With Multi-Resolution Training | Rui Tian, Zuxuan Wu, Qi Dai, Han Hu, Yu Qiao, Yu-Gang Jiang | Vision Transformers (ViTs) have achieved overwhelming success, yet they suffer from vulnerable resolution scalability, i.e., the performance drops drastically when presented with input resolutions that are unseen during training. We introduce, ResFormer, a framework that is built upon the seminal idea of multi-resoluti... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tian_ResFormer_Scaling_ViTs_With_Multi-Resolution_Training_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tian_ResFormer_Scaling_ViTs_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00776 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_ResFormer_Scaling_ViTs_With_Multi-Resolution_Training_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tian_ResFormer_Scaling_ViTs_With_Multi-Resolution_Training_CVPR_2023_paper.html | CVPR 2023 | null |
You Need Multiple Exiting: Dynamic Early Exiting for Accelerating Unified Vision Language Model | Shengkun Tang, Yaqing Wang, Zhenglun Kong, Tianchi Zhang, Yao Li, Caiwen Ding, Yanzhi Wang, Yi Liang, Dongkuan Xu | Large-scale transformer models bring significant improvements for various downstream vision language tasks with a unified architecture. The performance improvements come with increasing model size, resulting in slow inference speed and increased cost for severing. While some certain predictions benefit from the full co... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tang_You_Need_Multiple_Exiting_Dynamic_Early_Exiting_for_Accelerating_Unified_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.11152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_You_Need_Multiple_Exiting_Dynamic_Early_Exiting_for_Accelerating_Unified_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tang_You_Need_Multiple_Exiting_Dynamic_Early_Exiting_for_Accelerating_Unified_CVPR_2023_paper.html | CVPR 2023 | null |
CloSET: Modeling Clothed Humans on Continuous Surface With Explicit Template Decomposition | Hongwen Zhang, Siyou Lin, Ruizhi Shao, Yuxiang Zhang, Zerong Zheng, Han Huang, Yandong Guo, Yebin Liu | Creating animatable avatars from static scans requires the modeling of clothing deformations in different poses. Existing learning-based methods typically add pose-dependent deformations upon a minimally-clothed mesh template or a learned implicit template, which have limitations in capturing details or hinder end-to-e... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_CloSET_Modeling_Clothed_Humans_on_Continuous_Surface_With_Explicit_Template_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_CloSET_Modeling_Clothed_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.03167 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_CloSET_Modeling_Clothed_Humans_on_Continuous_Surface_With_Explicit_Template_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_CloSET_Modeling_Clothed_Humans_on_Continuous_Surface_With_Explicit_Template_CVPR_2023_paper.html | CVPR 2023 | null |
BUOL: A Bottom-Up Framework With Occupancy-Aware Lifting for Panoptic 3D Scene Reconstruction From a Single Image | Tao Chu, Pan Zhang, Qiong Liu, Jiaqi Wang | Understanding and modeling the 3D scene from a single image is a practical problem. A recent advance proposes a panoptic 3D scene reconstruction task that performs both 3D reconstruction and 3D panoptic segmentation from a single image. Although having made substantial progress, recent works only focus on top-down appr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chu_BUOL_A_Bottom-Up_Framework_With_Occupancy-Aware_Lifting_for_Panoptic_3D_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chu_BUOL_A_Bottom-Up_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chu_BUOL_A_Bottom-Up_Framework_With_Occupancy-Aware_Lifting_for_Panoptic_3D_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chu_BUOL_A_Bottom-Up_Framework_With_Occupancy-Aware_Lifting_for_Panoptic_3D_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Video-Moment Retrieval and Step-Captioning | Abhay Zala, Jaemin Cho, Satwik Kottur, Xilun Chen, Barlas Oguz, Yashar Mehdad, Mohit Bansal | There is growing interest in searching for information from large video corpora. Prior works have studied relevant tasks, such as text-based video retrieval, moment retrieval, video summarization, and video captioning in isolation, without an end-to-end setup that can jointly search from video corpora and generate summ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zala_Hierarchical_Video-Moment_Retrieval_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16406 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zala_Hierarchical_Video-Moment_Retrieval_and_Step-Captioning_CVPR_2023_paper.html | CVPR 2023 | null |
PROB: Probabilistic Objectness for Open World Object Detection | Orr Zohar, Kuan-Chieh Wang, Serena Yeung | Open World Object Detection (OWOD) is a new and challenging computer vision task that bridges the gap between classic object detection (OD) benchmarks and object detection in the real world. In addition to detecting and classifying seen/labeled objects, OWOD algorithms are expected to detect novel/unknown objects - whi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zohar_PROB_Probabilistic_Objectness_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.01424 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zohar_PROB_Probabilistic_Objectness_for_Open_World_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
PD-Quant: Post-Training Quantization Based on Prediction Difference Metric | Jiawei Liu, Lin Niu, Zhihang Yuan, Dawei Yang, Xinggang Wang, Wenyu Liu | Post-training quantization (PTQ) is a neural network compression technique that converts a full-precision model into a quantized model using lower-precision data types. Although it can help reduce the size and computational cost of deep neural networks, it can also introduce quantization noise and reduce prediction acc... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_PD-Quant_Post-Training_Quantization_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PD-Quant_Post-Training_Quantization_Based_on_Prediction_Difference_Metric_CVPR_2023_paper.html | CVPR 2023 | null |
AUNet: Learning Relations Between Action Units for Face Forgery Detection | Weiming Bai, Yufan Liu, Zhipeng Zhang, Bing Li, Weiming Hu | Face forgery detection becomes increasingly crucial due to the serious security issues caused by face manipulation techniques. Recent studies in deepfake detection have yielded promising results when the training and testing face forgeries are from the same domain. However, the problem remains challenging when one trie... | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bai_AUNet_Learning_Relations_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_AUNet_Learning_Relations_Between_Action_Units_for_Face_Forgery_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
SparseFusion: Distilling View-Conditioned Diffusion for 3D Reconstruction | Zhizhuo Zhou, Shubham Tulsiani | We propose SparseFusion, a sparse view 3D reconstruction approach that unifies recent advances in neural rendering and probabilistic image generation. Existing approaches typically build on neural rendering with re-projected features but fail to generate unseen regions or handle uncertainty under large viewpoint change... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_SparseFusion_Distilling_View-Conditioned_Diffusion_for_3D_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_SparseFusion_Distilling_View-Conditioned_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00792 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_SparseFusion_Distilling_View-Conditioned_Diffusion_for_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_SparseFusion_Distilling_View-Conditioned_Diffusion_for_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
PolyFormer: Referring Image Segmentation As Sequential Polygon Generation | Jiang Liu, Hui Ding, Zhaowei Cai, Yuting Zhang, Ravi Kumar Satzoda, Vijay Mahadevan, R. Manmatha | In this work, instead of directly predicting the pixel-level segmentation masks, the problem of referring image segmentation is formulated as sequential polygon generation, and the predicted polygons can be later converted into segmentation masks. This is enabled by a new sequence-to-sequence framework, Polygon Transfo... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_PolyFormer_Referring_Image_Segmentation_As_Sequential_Polygon_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_PolyFormer_Referring_Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.07387 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PolyFormer_Referring_Image_Segmentation_As_Sequential_Polygon_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_PolyFormer_Referring_Image_Segmentation_As_Sequential_Polygon_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
Seeing What You Miss: Vision-Language Pre-Training With Semantic Completion Learning | Yatai Ji, Rongcheng Tu, Jie Jiang, Weijie Kong, Chengfei Cai, Wenzhe Zhao, Hongfa Wang, Yujiu Yang, Wei Liu | Cross-modal alignment is essential for vision-language pre-training (VLP) models to learn the correct corresponding information across different modalities. For this purpose, inspired by the success of masked language modeling (MLM) tasks in the NLP pre-training area, numerous masked modeling tasks have been proposed f... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ji_Seeing_What_You_Miss_Vision-Language_Pre-Training_With_Semantic_Completion_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ji_Seeing_What_You_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.13437 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Seeing_What_You_Miss_Vision-Language_Pre-Training_With_Semantic_Completion_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Seeing_What_You_Miss_Vision-Language_Pre-Training_With_Semantic_Completion_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Interactive Segmentation As Gaussion Process Classification | Minghao Zhou, Hong Wang, Qian Zhao, Yuexiang Li, Yawen Huang, Deyu Meng, Yefeng Zheng | Click-based interactive segmentation (IS) aims to extract the target objects under user interaction. For this task, most of the current deep learning (DL)-based methods mainly follow the general pipelines of semantic segmentation. Albeit achieving promising performance, they do not fully and explicitly utilize and prop... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Interactive_Segmentation_As_Gaussion_Process_Classification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Interactive_Segmentation_As_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Interactive_Segmentation_As_Gaussion_Process_Classification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Interactive_Segmentation_As_Gaussion_Process_Classification_CVPR_2023_paper.html | CVPR 2023 | null |
Differentiable Shadow Mapping for Efficient Inverse Graphics | Markus Worchel, Marc Alexa | We show how shadows can be efficiently generated in differentiable rendering of triangle meshes. Our central observation is that pre-filtered shadow mapping, a technique for approximating shadows based on rendering from the perspective of a light, can be combined with existing differentiable rasterizers to yield differ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Worchel_Differentiable_Shadow_Mapping_for_Efficient_Inverse_Graphics_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Worchel_Differentiable_Shadow_Mapping_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Worchel_Differentiable_Shadow_Mapping_for_Efficient_Inverse_Graphics_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Worchel_Differentiable_Shadow_Mapping_for_Efficient_Inverse_Graphics_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Focus-Aware Positional Queries for Semantic Segmentation | Haoyu He, Jianfei Cai, Zizheng Pan, Jing Liu, Jing Zhang, Dacheng Tao, Bohan Zhuang | The DETR-like segmentors have underpinned the most recent breakthroughs in semantic segmentation, which end-to-end train a set of queries representing the class prototypes or target segments. Recently, masked attention is proposed to restrict each query to only attend to the foreground regions predicted by the precedin... | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Dynamic_Focus-Aware_Positional_Queries_for_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Dynamic_Focus-Aware_Positional_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.01244 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Dynamic_Focus-Aware_Positional_Queries_for_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Dynamic_Focus-Aware_Positional_Queries_for_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
A Practical Stereo Depth System for Smart Glasses | Jialiang Wang, Daniel Scharstein, Akash Bapat, Kevin Blackburn-Matzen, Matthew Yu, Jonathan Lehman, Suhib Alsisan, Yanghan Wang, Sam Tsai, Jan-Michael Frahm, Zijian He, Peter Vajda, Michael F. Cohen, Matt Uyttendaele | We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback to monocular depth estimation when rectification is unreliable. The output of our depth sensing system is then used in a novel view generati... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_A_Practical_Stereo_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.10551 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_A_Practical_Stereo_Depth_System_for_Smart_Glasses_CVPR_2023_paper.html | CVPR 2023 | null |
Understanding and Constructing Latent Modality Structures in Multi-Modal Representation Learning | Qian Jiang, Changyou Chen, Han Zhao, Liqun Chen, Qing Ping, Son Dinh Tran, Yi Xu, Belinda Zeng, Trishul Chilimbi | Contrastive loss has been increasingly used in learning representations from multiple modalities. In the limit, the nature of the contrastive loss encourages modalities to exactly match each other in the latent space. Yet it remains an open question how the modality alignment affects the downstream task performance. In... | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_Understanding_and_Constructing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05952 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Understanding_and_Constructing_Latent_Modality_Structures_in_Multi-Modal_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
PointConvFormer: Revenge of the Point-Based Convolution | Wenxuan Wu, Li Fuxin, Qi Shan | We introduce PointConvFormer, a novel building block for point cloud based deep network architectures. Inspired by generalization theory, PointConvFormer combines ideas from point convolution, where filter weights are only based on relative position, and Transformers which utilize feature-based attention. In PointConvF... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_PointConvFormer_Revenge_of_the_Point-Based_Convolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_PointConvFormer_Revenge_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.02879 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_PointConvFormer_Revenge_of_the_Point-Based_Convolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_PointConvFormer_Revenge_of_the_Point-Based_Convolution_CVPR_2023_paper.html | CVPR 2023 | null |
Instant Volumetric Head Avatars | Wojciech Zielonka, Timo Bolkart, Justus Thies | We present Instant Volumetric Head Avatars (INSTA), a novel approach for reconstructing photo-realistic digital avatars instantaneously. INSTA models a dynamic neural radiance field based on neural graphics primitives embedded around a parametric face model. Our pipeline is trained on a single monocular RGB portrait vi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zielonka_Instant_Volumetric_Head_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.12499 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zielonka_Instant_Volumetric_Head_Avatars_CVPR_2023_paper.html | CVPR 2023 | null |
HARP: Personalized Hand Reconstruction From a Monocular RGB Video | Korrawe Karunratanakul, Sergey Prokudin, Otmar Hilliges, Siyu Tang | We present HARP (HAnd Reconstruction and Personalization), a personalized hand avatar creation approach that takes a short monocular RGB video of a human hand as input and reconstructs a faithful hand avatar exhibiting a high-fidelity appearance and geometry. In contrast to the major trend of neural implicit representa... | https://openaccess.thecvf.com/content/CVPR2023/papers/Karunratanakul_HARP_Personalized_Hand_Reconstruction_From_a_Monocular_RGB_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Karunratanakul_HARP_Personalized_Hand_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09530 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Karunratanakul_HARP_Personalized_Hand_Reconstruction_From_a_Monocular_RGB_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Karunratanakul_HARP_Personalized_Hand_Reconstruction_From_a_Monocular_RGB_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Variational Distribution Learning for Unsupervised Text-to-Image Generation | Minsoo Kang, Doyup Lee, Jiseob Kim, Saehoon Kim, Bohyung Han | We propose a text-to-image generation algorithm based on deep neural networks when text captions for images are unavailable during training. In this work, instead of simply generating pseudo-ground-truth sentences of training images using existing image captioning methods, we employ a pretrained CLIP model, which is ca... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kang_Variational_Distribution_Learning_for_Unsupervised_Text-to-Image_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kang_Variational_Distribution_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16105 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Variational_Distribution_Learning_for_Unsupervised_Text-to-Image_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kang_Variational_Distribution_Learning_for_Unsupervised_Text-to-Image_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
MetaMix: Towards Corruption-Robust Continual Learning With Temporally Self-Adaptive Data Transformation | Zhenyi Wang, Li Shen, Donglin Zhan, Qiuling Suo, Yanjun Zhu, Tiehang Duan, Mingchen Gao | Continual Learning (CL) has achieved rapid progress in recent years. However, it is still largely unknown how to determine whether a CL model is trustworthy and how to foster its trustworthiness. This work focuses on evaluating and improving the robustness to corruptions of existing CL models. Our empirical evaluation ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MetaMix_Towards_Corruption-Robust_Continual_Learning_With_Temporally_Self-Adaptive_Data_Transformation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_MetaMix_Towards_Corruption-Robust_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MetaMix_Towards_Corruption-Robust_Continual_Learning_With_Temporally_Self-Adaptive_Data_Transformation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MetaMix_Towards_Corruption-Robust_Continual_Learning_With_Temporally_Self-Adaptive_Data_Transformation_CVPR_2023_paper.html | CVPR 2023 | null |
Ultra-High Resolution Segmentation With Ultra-Rich Context: A Novel Benchmark | Deyi Ji, Feng Zhao, Hongtao Lu, Mingyuan Tao, Jieping Ye | With the increasing interest and rapid development of methods for Ultra-High Resolution (UHR) segmentation, a large-scale benchmark covering a wide range of scenes with full fine-grained dense annotations is urgently needed to facilitate the field. To this end, the URUR dataset is introduced, in the meaning of Ultra-Hi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ji_Ultra-High_Resolution_Segmentation_With_Ultra-Rich_Context_A_Novel_Benchmark_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Ultra-High_Resolution_Segmentation_With_Ultra-Rich_Context_A_Novel_Benchmark_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ji_Ultra-High_Resolution_Segmentation_With_Ultra-Rich_Context_A_Novel_Benchmark_CVPR_2023_paper.html | CVPR 2023 | null |
DART: Diversify-Aggregate-Repeat Training Improves Generalization of Neural Networks | Samyak Jain, Sravanti Addepalli, Pawan Kumar Sahu, Priyam Dey, R. Venkatesh Babu | Generalization of Neural Networks is crucial for deploying them safely in the real world. Common training strategies to improve generalization involve the use of data augmentations, ensembling and model averaging. In this work, we first establish a surprisingly simple but strong benchmark for generalization which utili... | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_DART_Diversify-Aggregate-Repeat_Training_Improves_Generalization_of_Neural_Networks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_DART_Diversify-Aggregate-Repeat_Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.14685 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_DART_Diversify-Aggregate-Repeat_Training_Improves_Generalization_of_Neural_Networks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_DART_Diversify-Aggregate-Repeat_Training_Improves_Generalization_of_Neural_Networks_CVPR_2023_paper.html | CVPR 2023 | null |
Cross-Domain Image Captioning With Discriminative Finetuning | Roberto Dessì, Michele Bevilacqua, Eleonora Gualdoni, Nathanaël Carraz Rakotonirina, Francesca Franzon, Marco Baroni | Neural captioners are typically trained to mimic human-generated references without optimizing for any specific communication goal, leading to problems such as the generation of vague captions. In this paper, we show that fine-tuning an out-of-the-box neural captioner with a self-supervised discriminative communication... | https://openaccess.thecvf.com/content/CVPR2023/papers/Dessi_Cross-Domain_Image_Captioning_With_Discriminative_Finetuning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dessi_Cross-Domain_Image_Captioning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dessi_Cross-Domain_Image_Captioning_With_Discriminative_Finetuning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dessi_Cross-Domain_Image_Captioning_With_Discriminative_Finetuning_CVPR_2023_paper.html | CVPR 2023 | null |
Accelerating Vision-Language Pretraining With Free Language Modeling | Teng Wang, Yixiao Ge, Feng Zheng, Ran Cheng, Ying Shan, Xiaohu Qie, Ping Luo | The state of the arts in vision-language pretraining (VLP) achieves exemplary performance but suffers from high training costs resulting from slow convergence and long training time, especially on large-scale web datasets. An essential obstacle to training efficiency lies in the entangled prediction rate (percentage of... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Accelerating_Vision-Language_Pretraining_With_Free_Language_Modeling_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Accelerating_Vision-Language_Pretraining_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14038 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Accelerating_Vision-Language_Pretraining_With_Free_Language_Modeling_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Accelerating_Vision-Language_Pretraining_With_Free_Language_Modeling_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Mask Correction for Click-Based Interactive Image Segmentation | Fei Du, Jianlong Yuan, Zhibin Wang, Fan Wang | The goal of click-based interactive image segmentation is to extract target masks with the input of positive/negative clicks. Every time a new click is placed, existing methods run the whole segmentation network to obtain a corrected mask, which is inefficient since several clicks may be needed to reach satisfactory ac... | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Efficient_Mask_Correction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Efficient_Mask_Correction_for_Click-Based_Interactive_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
DBARF: Deep Bundle-Adjusting Generalizable Neural Radiance Fields | Yu Chen, Gim Hee Lee | Recent works such as BARF and GARF can bundle adjust camera poses with neural radiance fields (NeRF) which is based on coordinate-MLPs. Despite the impressive results, these methods cannot be applied to Generalizable NeRFs (GeNeRFs) which require image feature extractions that are often based on more complicated 3D CNN... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_DBARF_Deep_Bundle-Adjusting_Generalizable_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chen_DBARF_Deep_Bundle-Adjusting_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14478 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DBARF_Deep_Bundle-Adjusting_Generalizable_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_DBARF_Deep_Bundle-Adjusting_Generalizable_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
EvShutter: Transforming Events for Unconstrained Rolling Shutter Correction | Julius Erbach, Stepan Tulyakov, Patricia Vitoria, Alfredo Bochicchio, Yuanyou Li | Widely used Rolling Shutter (RS) CMOS sensors capture high resolution images at the expense of introducing distortions and artifacts in the presence of motion. In such situations, RS distortion correction algorithms are critical. Recent methods rely on a constant velocity assumption and require multiple frames to predi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Erbach_EvShutter_Transforming_Events_for_Unconstrained_Rolling_Shutter_Correction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Erbach_EvShutter_Transforming_Events_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Erbach_EvShutter_Transforming_Events_for_Unconstrained_Rolling_Shutter_Correction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Erbach_EvShutter_Transforming_Events_for_Unconstrained_Rolling_Shutter_Correction_CVPR_2023_paper.html | CVPR 2023 | null |
Graphics Capsule: Learning Hierarchical 3D Face Representations From 2D Images | Chang Yu, Xiangyu Zhu, Xiaomei Zhang, Zhaoxiang Zhang, Zhen Lei | The function of constructing the hierarchy of objects is important to the visual process of the human brain. Previous studies have successfully adopted capsule networks to decompose the digits and faces into parts in an unsupervised manner to investigate the similar perception mechanism of neural networks. However, the... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Graphics_Capsule_Learning_Hierarchical_3D_Face_Representations_From_2D_Images_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Graphics_Capsule_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10896 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Graphics_Capsule_Learning_Hierarchical_3D_Face_Representations_From_2D_Images_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Graphics_Capsule_Learning_Hierarchical_3D_Face_Representations_From_2D_Images_CVPR_2023_paper.html | CVPR 2023 | null |
Connecting the Dots: Floorplan Reconstruction Using Two-Level Queries | Yuanwen Yue, Theodora Kontogianni, Konrad Schindler, Francis Engelmann | We address 2D floorplan reconstruction from 3D scans. Existing approaches typically employ heuristically designed multi-stage pipelines. Instead, we formulate floorplan reconstruction as a single-stage structured prediction task: find a variable-size set of polygons, which in turn are variable-length sequences of order... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yue_Connecting_the_Dots_Floorplan_Reconstruction_Using_Two-Level_Queries_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yue_Connecting_the_Dots_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15658 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yue_Connecting_the_Dots_Floorplan_Reconstruction_Using_Two-Level_Queries_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yue_Connecting_the_Dots_Floorplan_Reconstruction_Using_Two-Level_Queries_CVPR_2023_paper.html | CVPR 2023 | null |
Analyzing and Diagnosing Pose Estimation With Attributions | Qiyuan He, Linlin Yang, Kerui Gu, Qiuxia Lin, Angela Yao | We present Pose Integrated Gradient (PoseIG), the first interpretability technique designed for pose estimation. We extend the concept of integrated gradients for pose estimation to generate pixel-level attribution maps. To enable comparison across different pose frameworks, we unify different pose outputs into a commo... | https://openaccess.thecvf.com/content/CVPR2023/papers/He_Analyzing_and_Diagnosing_Pose_Estimation_With_Attributions_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/He_Analyzing_and_Diagnosing_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/He_Analyzing_and_Diagnosing_Pose_Estimation_With_Attributions_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/He_Analyzing_and_Diagnosing_Pose_Estimation_With_Attributions_CVPR_2023_paper.html | CVPR 2023 | null |
Ambiguity-Resistant Semi-Supervised Learning for Dense Object Detection | Chang Liu, Weiming Zhang, Xiangru Lin, Wei Zhang, Xiao Tan, Junyu Han, Xiaomao Li, Errui Ding, Jingdong Wang | With basic Semi-Supervised Object Detection (SSOD) techniques, one-stage detectors generally obtain limited promotions compared with two-stage clusters. We experimentally find that the root lies in two kinds of ambiguities: (1) Selection ambiguity that selected pseudo labels are less accurate, since classification scor... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14960 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Ambiguity-Resistant_Semi-Supervised_Learning_for_Dense_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Scalable, Detailed and Mask-Free Universal Photometric Stereo | Satoshi Ikehata | In this paper, we introduce SDM-UniPS, a groundbreaking Scalable, Detailed, Mask-free, and Universal Photometric Stereo network. Our approach can recover astonishingly intricate surface normal maps, rivaling the quality of 3D scanners, even when images are captured under unknown, spatially-varying lighting conditions i... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ikehata_Scalable_Detailed_and_Mask-Free_Universal_Photometric_Stereo_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ikehata_Scalable_Detailed_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.15724 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ikehata_Scalable_Detailed_and_Mask-Free_Universal_Photometric_Stereo_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ikehata_Scalable_Detailed_and_Mask-Free_Universal_Photometric_Stereo_CVPR_2023_paper.html | CVPR 2023 | null |
Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting | Gen Li, Jie Ji, Minghai Qin, Wei Niu, Bin Ren, Fatemeh Afghah, Linke Guo, Xiaolong Ma | As deep convolutional neural networks (DNNs) are widely used in various fields of computer vision, leveraging the overfitting ability of the DNN to achieve video resolution upscaling has become a new trend in the modern video delivery system. By dividing videos into chunks and overfitting each chunk with a super-resolu... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Towards_High-Quality_and_Efficient_Video_Super-Resolution_via_Spatial-Temporal_Data_Overfitting_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08331 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Towards_High-Quality_and_Efficient_Video_Super-Resolution_via_Spatial-Temporal_Data_Overfitting_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Towards_High-Quality_and_Efficient_Video_Super-Resolution_via_Spatial-Temporal_Data_Overfitting_CVPR_2023_paper.html | CVPR 2023 | null |
Make-a-Story: Visual Memory Conditioned Consistent Story Generation | Tanzila Rahman, Hsin-Ying Lee, Jian Ren, Sergey Tulyakov, Shweta Mahajan, Leonid Sigal | There has been a recent explosion of impressive generative models that can produce high quality images (or videos) conditioned on text descriptions. However, all such approaches rely on conditional sentences that contain unambiguous descriptions of scenes and main actors in them. Therefore employing such models for mor... | https://openaccess.thecvf.com/content/CVPR2023/papers/Rahman_Make-a-Story_Visual_Memory_Conditioned_Consistent_Story_Generation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rahman_Make-a-Story_Visual_Memory_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rahman_Make-a-Story_Visual_Memory_Conditioned_Consistent_Story_Generation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rahman_Make-a-Story_Visual_Memory_Conditioned_Consistent_Story_Generation_CVPR_2023_paper.html | CVPR 2023 | null |
BiFormer: Vision Transformer With Bi-Level Routing Attention | Lei Zhu, Xinjiang Wang, Zhanghan Ke, Wayne Zhang, Rynson W.H. Lau | As the core building block of vision transformers, attention is a powerful tool to capture long-range dependency. However, such power comes at a cost: it incurs a huge computation burden and heavy memory footprint as pairwise token interaction across all spatial locations is computed. A series of works attempt to allev... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_BiFormer_Vision_Transformer_With_Bi-Level_Routing_Attention_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_BiFormer_Vision_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08810 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_BiFormer_Vision_Transformer_With_Bi-Level_Routing_Attention_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_BiFormer_Vision_Transformer_With_Bi-Level_Routing_Attention_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Autoencoders Enable Efficient Knowledge Distillers | Yutong Bai, Zeyu Wang, Junfei Xiao, Chen Wei, Huiyu Wang, Alan L. Yuille, Yuyin Zhou, Cihang Xie | This paper studies the potential of distilling knowledge from pre-trained models, especially Masked Autoencoders. Our approach is simple: in addition to optimizing the pixel reconstruction loss on masked inputs, we minimize the distance between the intermediate feature map of the teacher model and that of the student m... | https://openaccess.thecvf.com/content/CVPR2023/papers/Bai_Masked_Autoencoders_Enable_Efficient_Knowledge_Distillers_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2208.12256 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Masked_Autoencoders_Enable_Efficient_Knowledge_Distillers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bai_Masked_Autoencoders_Enable_Efficient_Knowledge_Distillers_CVPR_2023_paper.html | CVPR 2023 | null |
TinyMIM: An Empirical Study of Distilling MIM Pre-Trained Models | Sucheng Ren, Fangyun Wei, Zheng Zhang, Han Hu | Masked image modeling (MIM) performs strongly in pre-training large vision Transformers (ViTs). However, small models that are critical for real-world applications cannot or only marginally benefit from this pre-training approach. In this paper, we explore distillation techniques to transfer the success of large MIM-ba... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ren_TinyMIM_An_Empirical_Study_of_Distilling_MIM_Pre-Trained_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ren_TinyMIM_An_Empirical_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01296 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_TinyMIM_An_Empirical_Study_of_Distilling_MIM_Pre-Trained_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ren_TinyMIM_An_Empirical_Study_of_Distilling_MIM_Pre-Trained_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Persistent Nature: A Generative Model of Unbounded 3D Worlds | Lucy Chai, Richard Tucker, Zhengqi Li, Phillip Isola, Noah Snavely | Despite increasingly realistic image quality, recent 3D image generative models often operate on 3D volumes of fixed extent with limited camera motions. We investigate the task of unconditionally synthesizing unbounded nature scenes, enabling arbitrarily large camera motion while maintaining a persistent 3D world model... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chai_Persistent_Nature_A_Generative_Model_of_Unbounded_3D_Worlds_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chai_Persistent_Nature_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13515 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chai_Persistent_Nature_A_Generative_Model_of_Unbounded_3D_Worlds_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chai_Persistent_Nature_A_Generative_Model_of_Unbounded_3D_Worlds_CVPR_2023_paper.html | CVPR 2023 | null |
OneFormer: One Transformer To Rule Universal Image Segmentation | Jitesh Jain, Jiachen Li, Mang Tik Chiu, Ali Hassani, Nikita Orlov, Humphrey Shi | Universal Image Segmentation is not a new concept.Past attempts to unify image segmentation include scene parsing, panoptic segmentation, and, more recently, new panoptic architectures. However, such panoptic architectures do not truly unify image segmentation because they need to be trained individually on the semanti... | https://openaccess.thecvf.com/content/CVPR2023/papers/Jain_OneFormer_One_Transformer_To_Rule_Universal_Image_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jain_OneFormer_One_Transformer_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.06220 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_OneFormer_One_Transformer_To_Rule_Universal_Image_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jain_OneFormer_One_Transformer_To_Rule_Universal_Image_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Hierarchical Neural Memory Network for Low Latency Event Processing | Ryuhei Hamaguchi, Yasutaka Furukawa, Masaki Onishi, Ken Sakurada | This paper proposes a low latency neural network architecture for event-based dense prediction tasks. Conventional architectures encode entire scene contents at a fixed rate regardless of their temporal characteristics. Instead, the proposed network encodes contents at a proper temporal scale depending on its movement ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Hamaguchi_Hierarchical_Neural_Memory_Network_for_Low_Latency_Event_Processing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Hamaguchi_Hierarchical_Neural_Memory_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Hamaguchi_Hierarchical_Neural_Memory_Network_for_Low_Latency_Event_Processing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Hamaguchi_Hierarchical_Neural_Memory_Network_for_Low_Latency_Event_Processing_CVPR_2023_paper.html | CVPR 2023 | null |
Finding Geometric Models by Clustering in the Consensus Space | Daniel Barath, Denys Rozumnyi, Ivan Eichhardt, Levente Hajder, Jiri Matas | We propose a new algorithm for finding an unknown number of geometric models, e.g., homographies. The problem is formalized as finding dominant model instances progressively without forming crisp point-to-model assignments. Dominant instances are found via a RANSAC-like sampling and a consolidation process driven by a ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Barath_Finding_Geometric_Models_by_Clustering_in_the_Consensus_Space_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Barath_Finding_Geometric_Models_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Barath_Finding_Geometric_Models_by_Clustering_in_the_Consensus_Space_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Barath_Finding_Geometric_Models_by_Clustering_in_the_Consensus_Space_CVPR_2023_paper.html | CVPR 2023 | null |
Leapfrog Diffusion Model for Stochastic Trajectory Prediction | Weibo Mao, Chenxin Xu, Qi Zhu, Siheng Chen, Yanfeng Wang | To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories. Emerging diffusion models have revealed their tremendous representation capacities in numerous generation tasks, showing potential for stochastic trajectory predictio... | https://openaccess.thecvf.com/content/CVPR2023/papers/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Mao_Leapfrog_Diffusion_Model_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.10895 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Mao_Leapfrog_Diffusion_Model_for_Stochastic_Trajectory_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
DaFKD: Domain-Aware Federated Knowledge Distillation | Haozhao Wang, Yichen Li, Wenchao Xu, Ruixuan Li, Yufeng Zhan, Zhigang Zeng | Federated Distillation (FD) has recently attracted increasing attention for its efficiency in aggregating multiple diverse local models trained from statistically heterogeneous data of distributed clients. Existing FD methods generally treat these models equally by merely computing the average of their output soft pred... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_DaFKD_Domain-Aware_Federated_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_DaFKD_Domain-Aware_Federated_Knowledge_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
GeoLayoutLM: Geometric Pre-Training for Visual Information Extraction | Chuwei Luo, Changxu Cheng, Qi Zheng, Cong Yao | Visual information extraction (VIE) plays an important role in Document Intelligence. Generally, it is divided into two tasks: semantic entity recognition (SER) and relation extraction (RE). Recently, pre-trained models for documents have achieved substantial progress in VIE, particularly in SER. However, most of the e... | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.10759 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_GeoLayoutLM_Geometric_Pre-Training_for_Visual_Information_Extraction_CVPR_2023_paper.html | CVPR 2023 | null |
Class-Incremental Exemplar Compression for Class-Incremental Learning | Zilin Luo, Yaoyao Liu, Bernt Schiele, Qianru Sun | Exemplar-based class-incremental learning (CIL) finetunes the model with all samples of new classes but few-shot exemplars of old classes in each incremental phase, where the "few-shot" abides by the limited memory budget. In this paper, we break this "few-shot" limit based on a simple yet surprisingly effective idea: ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_Class-Incremental_Exemplar_Compression_for_Class-Incremental_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Luo_Class-Incremental_Exemplar_Compression_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14042 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Class-Incremental_Exemplar_Compression_for_Class-Incremental_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_Class-Incremental_Exemplar_Compression_for_Class-Incremental_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Boost Vision Transformer With GPU-Friendly Sparsity and Quantization | Chong Yu, Tao Chen, Zhongxue Gan, Jiayuan Fan | The transformer extends its success from the language to the vision domain. Because of the numerous stacked self-attention and cross-attention blocks in the transformer, which involve many high-dimensional tensor multiplication operations, the acceleration deployment of vision transformer on GPU hardware is challenging... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Boost_Vision_Transformer_With_GPU-Friendly_Sparsity_and_Quantization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Boost_Vision_Transformer_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Boost_Vision_Transformer_With_GPU-Friendly_Sparsity_and_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Boost_Vision_Transformer_With_GPU-Friendly_Sparsity_and_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
Spectral Bayesian Uncertainty for Image Super-Resolution | Tao Liu, Jun Cheng, Shan Tan | Recently deep learning techniques have significantly advanced image super-resolution (SR). Due to the black-box nature, quantifying reconstruction uncertainty is crucial when employing these deep SR networks. Previous approaches for SR uncertainty estimation mostly focus on capturing pixel-wise uncertainty in the spati... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Spectral_Bayesian_Uncertainty_for_Image_Super-Resolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Spectral_Bayesian_Uncertainty_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Spectral_Bayesian_Uncertainty_for_Image_Super-Resolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Spectral_Bayesian_Uncertainty_for_Image_Super-Resolution_CVPR_2023_paper.html | CVPR 2023 | null |
Behind the Scenes: Density Fields for Single View Reconstruction | Felix Wimbauer, Nan Yang, Christian Rupprecht, Daniel Cremers | Inferring a meaningful geometric scene representation from a single image is a fundamental problem in computer vision. Approaches based on traditional depth map prediction can only reason about areas that are visible in the image. Currently, neural radiance fields (NeRFs) can capture true 3D including color, but are to... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wimbauer_Behind_the_Scenes_Density_Fields_for_Single_View_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wimbauer_Behind_the_Scenes_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.07668 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wimbauer_Behind_the_Scenes_Density_Fields_for_Single_View_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wimbauer_Behind_the_Scenes_Density_Fields_for_Single_View_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
StyleGAN Salon: Multi-View Latent Optimization for Pose-Invariant Hairstyle Transfer | Sasikarn Khwanmuang, Pakkapon Phongthawee, Patsorn Sangkloy, Supasorn Suwajanakorn | Our paper seeks to transfer the hairstyle of a reference image to an input photo for virtual hair try-on. We target a variety of challenges scenarios, such as transforming a long hairstyle with bangs to a pixie cut, which requires removing the existing hair and inferring how the forehead would look, or transferring par... | https://openaccess.thecvf.com/content/CVPR2023/papers/Khwanmuang_StyleGAN_Salon_Multi-View_Latent_Optimization_for_Pose-Invariant_Hairstyle_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Khwanmuang_StyleGAN_Salon_Multi-View_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.02744 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Khwanmuang_StyleGAN_Salon_Multi-View_Latent_Optimization_for_Pose-Invariant_Hairstyle_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Khwanmuang_StyleGAN_Salon_Multi-View_Latent_Optimization_for_Pose-Invariant_Hairstyle_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
Resource-Efficient RGBD Aerial Tracking | Jinyu Yang, Shang Gao, Zhe Li, Feng Zheng, Aleš Leonardis | Aerial robots are now able to fly in complex environments, and drone-captured data gains lots of attention in object tracking. However, current research on aerial perception has mainly focused on limited categories, such as pedestrian or vehicle, and most scenes are captured in urban environments from a birds-eye view.... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Resource-Efficient_RGBD_Aerial_Tracking_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Resource-Efficient_RGBD_Aerial_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Resource-Efficient_RGBD_Aerial_Tracking_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Resource-Efficient_RGBD_Aerial_Tracking_CVPR_2023_paper.html | CVPR 2023 | null |
Mutual Information-Based Temporal Difference Learning for Human Pose Estimation in Video | Runyang Feng, Yixing Gao, Xueqing Ma, Tze Ho Elden Tse, Hyung Jin Chang | Temporal modeling is crucial for multi-frame human pose estimation. Most existing methods directly employ optical flow or deformable convolution to predict full-spectrum motion fields, which might incur numerous irrelevant cues, such as a nearby person or background. Without further efforts to excavate meaningful motio... | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Mutual_Information-Based_Temporal_Difference_Learning_for_Human_Pose_Estimation_in_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.08475 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Mutual_Information-Based_Temporal_Difference_Learning_for_Human_Pose_Estimation_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Mutual_Information-Based_Temporal_Difference_Learning_for_Human_Pose_Estimation_in_CVPR_2023_paper.html | CVPR 2023 | null |
Bilateral Memory Consolidation for Continual Learning | Xing Nie, Shixiong Xu, Xiyan Liu, Gaofeng Meng, Chunlei Huo, Shiming Xiang | Humans are proficient at continuously acquiring and integrating new knowledge. By contrast, deep models forget catastrophically, especially when tackling highly long task sequences. Inspired by the way our brains constantly rewrite and consolidate past recollections, we propose a novel Bilateral Memory Consolidation (B... | https://openaccess.thecvf.com/content/CVPR2023/papers/Nie_Bilateral_Memory_Consolidation_for_Continual_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nie_Bilateral_Memory_Consolidation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nie_Bilateral_Memory_Consolidation_for_Continual_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nie_Bilateral_Memory_Consolidation_for_Continual_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
SynthVSR: Scaling Up Visual Speech Recognition With Synthetic Supervision | Xubo Liu, Egor Lakomkin, Konstantinos Vougioukas, Pingchuan Ma, Honglie Chen, Ruiming Xie, Morrie Doulaty, Niko Moritz, Jachym Kolar, Stavros Petridis, Maja Pantic, Christian Fuegen | Recently reported state-of-the-art results in visual speech recognition (VSR) often rely on increasingly large amounts of video data, while the publicly available transcribed video datasets are limited in size. In this paper, for the first time, we study the potential of leveraging synthetic visual data for VSR. Our me... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_SynthVSR_Scaling_Up_Visual_Speech_Recognition_With_Synthetic_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_SynthVSR_Scaling_Up_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.17200 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SynthVSR_Scaling_Up_Visual_Speech_Recognition_With_Synthetic_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_SynthVSR_Scaling_Up_Visual_Speech_Recognition_With_Synthetic_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
BiasBed - Rigorous Texture Bias Evaluation | Nikolai Kalischek, Rodrigo Caye Daudt, Torben Peters, Reinhard Furrer, Jan D. Wegner, Konrad Schindler | The well-documented presence of texture bias in modern convolutional neural networks has led to a plethora of algorithms that promote an emphasis on shape cues, often to support generalization to new domains. Yet, common datasets, benchmarks and general model selection strategies are missing, and there is no agreed, ri... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kalischek_BiasBed_-_Rigorous_Texture_Bias_Evaluation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kalischek_BiasBed_-_Rigorous_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kalischek_BiasBed_-_Rigorous_Texture_Bias_Evaluation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kalischek_BiasBed_-_Rigorous_Texture_Bias_Evaluation_CVPR_2023_paper.html | CVPR 2023 | null |
Open-Category Human-Object Interaction Pre-Training via Language Modeling Framework | Sipeng Zheng, Boshen Xu, Qin Jin | Human-object interaction (HOI) has long been plagued by the conflict between limited supervised data and a vast number of possible interaction combinations in real life. Current methods trained from closed-set data predict HOIs as fixed-dimension logits, which restricts their scalability to open-set categories. To addr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zheng_Open-Category_Human-Object_Interaction_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_Open-Category_Human-Object_Interaction_Pre-Training_via_Language_Modeling_Framework_CVPR_2023_paper.html | CVPR 2023 | null |
SFD2: Semantic-Guided Feature Detection and Description | Fei Xue, Ignas Budvytis, Roberto Cipolla | Visual localization is a fundamental task for various applications including autonomous driving and robotics. Prior methods focus on extracting large amounts of often redundant locally reliable features, resulting in limited efficiency and accuracy, especially in large-scale environments under challenging conditions. I... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xue_SFD2_Semantic-Guided_Feature_Detection_and_Description_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xue_SFD2_Semantic-Guided_Feature_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14845 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_SFD2_Semantic-Guided_Feature_Detection_and_Description_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xue_SFD2_Semantic-Guided_Feature_Detection_and_Description_CVPR_2023_paper.html | CVPR 2023 | null |
Search-Map-Search: A Frame Selection Paradigm for Action Recognition | Mingjun Zhao, Yakun Yu, Xiaoli Wang, Lei Yang, Di Niu | Despite the success of deep learning in video understanding tasks, processing every frame in a video is computationally expensive and often unnecessary in real-time applications. Frame selection aims to extract the most informative and representative frames to help a model better understand video content. Existing fram... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Search-Map-Search_A_Frame_Selection_Paradigm_for_Action_Recognition_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Search-Map-Search_A_Frame_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Search-Map-Search_A_Frame_Selection_Paradigm_for_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Search-Map-Search_A_Frame_Selection_Paradigm_for_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Uncovering the Missing Pattern: Unified Framework Towards Trajectory Imputation and Prediction | Yi Xu, Armin Bazarjani, Hyung-gun Chi, Chiho Choi, Yun Fu | Trajectory prediction is a crucial undertaking in understanding entity movement or human behavior from observed sequences. However, current methods often assume that the observed sequences are complete while ignoring the potential for missing values caused by object occlusion, scope limitation, sensor failure, etc. Thi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Uncovering_the_Missing_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.16005 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Uncovering_the_Missing_Pattern_Unified_Framework_Towards_Trajectory_Imputation_and_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP for All Things Zero-Shot Sketch-Based Image Retrieval, Fine-Grained or Not | Aneeshan Sain, Ayan Kumar Bhunia, Pinaki Nath Chowdhury, Subhadeep Koley, Tao Xiang, Yi-Zhe Song | In this paper, we leverage CLIP for zero-shot sketch based image retrieval (ZS-SBIR). We are largely inspired by recent advances on foundation models and the unparalleled generalisation ability they seem to offer, but for the first time tailor it to benefit the sketch community. We put forward novel designs on how best... | https://openaccess.thecvf.com/content/CVPR2023/papers/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sain_CLIP_for_All_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13440 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sain_CLIP_for_All_Things_Zero-Shot_Sketch-Based_Image_Retrieval_Fine-Grained_or_CVPR_2023_paper.html | CVPR 2023 | null |
FlexiViT: One Model for All Patch Sizes | Lucas Beyer, Pavel Izmailov, Alexander Kolesnikov, Mathilde Caron, Simon Kornblith, Xiaohua Zhai, Matthias Minderer, Michael Tschannen, Ibrahim Alabdulmohsin, Filip Pavetic | Vision Transformers convert images to sequences by slicing them into patches. The size of these patches controls a speed/accuracy tradeoff, with smaller patches leading to higher accuracy at greater computational cost, but changing the patch size typically requires retraining the model. In this paper, we demonstrate th... | https://openaccess.thecvf.com/content/CVPR2023/papers/Beyer_FlexiViT_One_Model_for_All_Patch_Sizes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Beyer_FlexiViT_One_Model_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08013 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Beyer_FlexiViT_One_Model_for_All_Patch_Sizes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Beyer_FlexiViT_One_Model_for_All_Patch_Sizes_CVPR_2023_paper.html | CVPR 2023 | null |
RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereo | Changjiang Cai, Pan Ji, Qingan Yan, Yi Xu | This paper presents a learning-based method for multi-view depth estimation from posed images. Our core idea is a "learning-to-optimize" paradigm that iteratively indexes a plane-sweeping cost volume and regresses the depth map via a convolutional Gated Recurrent Unit (GRU). Since the cost volume plays a paramount role... | https://openaccess.thecvf.com/content/CVPR2023/papers/Cai_RIAV-MVS_Recurrent-Indexing_an_Asymmetric_Volume_for_Multi-View_Stereo_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cai_RIAV-MVS_Recurrent-Indexing_an_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_RIAV-MVS_Recurrent-Indexing_an_Asymmetric_Volume_for_Multi-View_Stereo_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cai_RIAV-MVS_Recurrent-Indexing_an_Asymmetric_Volume_for_Multi-View_Stereo_CVPR_2023_paper.html | CVPR 2023 | null |
Structured Kernel Estimation for Photon-Limited Deconvolution | Yash Sanghvi, Zhiyuan Mao, Stanley H. Chan | Images taken in a low light condition with the presence of camera shake suffer from motion blur and photon shot noise. While state-of-the-art image restoration networks show promising results, they are largely limited to well-illuminated scenes and their performance drops significantly when photon shot noise is strong.... | https://openaccess.thecvf.com/content/CVPR2023/papers/Sanghvi_Structured_Kernel_Estimation_for_Photon-Limited_Deconvolution_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sanghvi_Structured_Kernel_Estimation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.03472 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sanghvi_Structured_Kernel_Estimation_for_Photon-Limited_Deconvolution_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sanghvi_Structured_Kernel_Estimation_for_Photon-Limited_Deconvolution_CVPR_2023_paper.html | CVPR 2023 | null |
Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection | Xincheng Yao, Ruoqi Li, Jing Zhang, Jun Sun, Chongyang Zhang | Most anomaly detection (AD) models are learned using only normal samples in an unsupervised way, which may result in ambiguous decision boundary and insufficient discriminability. In fact, a few anomaly samples are often available in real-world applications, the valuable knowledge of known anomalies should also be effe... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yao_Explicit_Boundary_Guided_Semi-Push-Pull_Contrastive_Learning_for_Supervised_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yao_Explicit_Boundary_Guided_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2207.01463 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Explicit_Boundary_Guided_Semi-Push-Pull_Contrastive_Learning_for_Supervised_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yao_Explicit_Boundary_Guided_Semi-Push-Pull_Contrastive_Learning_for_Supervised_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
3D Video Loops From Asynchronous Input | Li Ma, Xiaoyu Li, Jing Liao, Pedro V. Sander | Looping videos are short video clips that can be looped endlessly without visible seams or artifacts. They provide a very attractive way to capture the dynamism of natural scenes. Existing methods have been mostly limited to 2D representations. In this paper, we take a step forward and propose a practical solution that... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_3D_Video_Loops_From_Asynchronous_Input_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_3D_Video_Loops_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.05312 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_3D_Video_Loops_From_Asynchronous_Input_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_3D_Video_Loops_From_Asynchronous_Input_CVPR_2023_paper.html | CVPR 2023 | null |
Style Projected Clustering for Domain Generalized Semantic Segmentation | Wei Huang, Chang Chen, Yong Li, Jiacheng Li, Cheng Li, Fenglong Song, Youliang Yan, Zhiwei Xiong | Existing semantic segmentation methods improve generalization capability, by regularizing various images to a canonical feature space. While this process contributes to generalization, it weakens the representation inevitably. In contrast to existing methods, we instead utilize the difference between images to build a ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Style_Projected_Clustering_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Style_Projected_Clustering_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Style_Projected_Clustering_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Style_Projected_Clustering_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
DIP: Dual Incongruity Perceiving Network for Sarcasm Detection | Changsong Wen, Guoli Jia, Jufeng Yang | Sarcasm indicates the literal meaning is contrary to the real attitude. Considering the popularity and complementarity of image-text data, we investigate the task of multi-modal sarcasm detection. Different from other multi-modal tasks, for the sarcastic data, there exists intrinsic incongruity between a pair of image ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wen_DIP_Dual_Incongruity_Perceiving_Network_for_Sarcasm_Detection_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_DIP_Dual_Incongruity_Perceiving_Network_for_Sarcasm_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wen_DIP_Dual_Incongruity_Perceiving_Network_for_Sarcasm_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Frame Interpolation Transformer and Uncertainty Guidance | Markus Plack, Karlis Martins Briedis, Abdelaziz Djelouah, Matthias B. Hullin, Markus Gross, Christopher Schroers | Video frame interpolation has seen important progress in recent years, thanks to developments in several directions. Some works leverage better optical flow methods with improved splatting strategies or additional cues from depth, while others have investigated alternative approaches through direct predictions or trans... | https://openaccess.thecvf.com/content/CVPR2023/papers/Plack_Frame_Interpolation_Transformer_and_Uncertainty_Guidance_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Plack_Frame_Interpolation_Transformer_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Plack_Frame_Interpolation_Transformer_and_Uncertainty_Guidance_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Plack_Frame_Interpolation_Transformer_and_Uncertainty_Guidance_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Generate Language-Supervised and Open-Vocabulary Scene Graph Using Pre-Trained Visual-Semantic Space | Yong Zhang, Yingwei Pan, Ting Yao, Rui Huang, Tao Mei, Chang-Wen Chen | Scene graph generation (SGG) aims to abstract an image into a graph structure, by representing objects as graph nodes and their relations as labeled edges. However, two knotty obstacles limit the practicability of current SGG methods in real-world scenarios: 1) training SGG models requires time-consuming ground-truth a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Learning_To_Generate_Language-Supervised_and_Open-Vocabulary_Scene_Graph_Using_Pre-Trained_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Learning_To_Generate_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_To_Generate_Language-Supervised_and_Open-Vocabulary_Scene_Graph_Using_Pre-Trained_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Learning_To_Generate_Language-Supervised_and_Open-Vocabulary_Scene_Graph_Using_Pre-Trained_CVPR_2023_paper.html | CVPR 2023 | null |
VectorFloorSeg: Two-Stream Graph Attention Network for Vectorized Roughcast Floorplan Segmentation | Bingchen Yang, Haiyong Jiang, Hao Pan, Jun Xiao | Vector graphics (VG) are ubiquitous in industrial designs. In this paper, we address semantic segmentation of a typical VG, i.e., roughcast floorplans with bare wall structures, whose output can be directly used for further applications like interior furnishing and room space modeling. Previous semantic segmentation wo... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_VectorFloorSeg_Two-Stream_Graph_Attention_Network_for_Vectorized_Roughcast_Floorplan_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_VectorFloorSeg_Two-Stream_Graph_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_VectorFloorSeg_Two-Stream_Graph_Attention_Network_for_Vectorized_Roughcast_Floorplan_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_VectorFloorSeg_Two-Stream_Graph_Attention_Network_for_Vectorized_Roughcast_Floorplan_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Neural Preset for Color Style Transfer | Zhanghan Ke, Yuhao Liu, Lei Zhu, Nanxuan Zhao, Rynson W.H. Lau | In this paper, we present a Neural Preset technique to address the limitations of existing color style transfer methods, including visual artifacts, vast memory requirement, and slow style switching speed. Our method is based on two core designs. First, we propose Deterministic Neural Color Mapping (DNCM) to consistent... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ke_Neural_Preset_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ke_Neural_Preset_for_Color_Style_Transfer_CVPR_2023_paper.html | CVPR 2023 | null |
DeCo: Decomposition and Reconstruction for Compositional Temporal Grounding via Coarse-To-Fine Contrastive Ranking | Lijin Yang, Quan Kong, Hsuan-Kung Yang, Wadim Kehl, Yoichi Sato, Norimasa Kobori | Understanding dense action in videos is a fundamental challenge towards the generalization of vision models. Several works show that compositionality is key to achieving generalization by combining known primitive elements, especially for handling novel composited structures. Compositional temporal grounding is the tas... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_DeCo_Decomposition_and_Reconstruction_for_Compositional_Temporal_Grounding_via_Coarse-To-Fine_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_DeCo_Decomposition_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_DeCo_Decomposition_and_Reconstruction_for_Compositional_Temporal_Grounding_via_Coarse-To-Fine_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_DeCo_Decomposition_and_Reconstruction_for_Compositional_Temporal_Grounding_via_Coarse-To-Fine_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamic Aggregated Network for Gait Recognition | Kang Ma, Ying Fu, Dezhi Zheng, Chunshui Cao, Xuecai Hu, Yongzhen Huang | Gait recognition is beneficial for a variety of applications, including video surveillance, crime scene investigation, and social security, to mention a few. However, gait recognition often suffers from multiple exterior factors in real scenes, such as carrying conditions, wearing overcoats, and diverse viewing angles.... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Dynamic_Aggregated_Network_for_Gait_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Wavelet Diffusion Models Are Fast and Scalable Image Generators | Hao Phung, Quan Dao, Anh Tran | Diffusion models are rising as a powerful solution for high-fidelity image generation, which exceeds GANs in quality in many circumstances. However, their slow training and inference speed is a huge bottleneck, blocking them from being used in real-time applications. A recent DiffusionGAN method significantly decreases... | https://openaccess.thecvf.com/content/CVPR2023/papers/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Phung_Wavelet_Diffusion_Models_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16152 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Phung_Wavelet_Diffusion_Models_Are_Fast_and_Scalable_Image_Generators_CVPR_2023_paper.html | CVPR 2023 | null |
PA&DA: Jointly Sampling Path and Data for Consistent NAS | Shun Lu, Yu Hu, Longxing Yang, Zihao Sun, Jilin Mei, Jianchao Tan, Chengru Song | Based on the weight-sharing mechanism, one-shot NAS methods train a supernet and then inherit the pre-trained weights to evaluate sub-models, largely reducing the search cost. However, several works have pointed out that the shared weights suffer from different gradient descent directions during training. And we furthe... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_PADA_Jointly_Sampling_Path_and_Data_for_Consistent_NAS_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lu_PADA_Jointly_Sampling_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_PADA_Jointly_Sampling_Path_and_Data_for_Consistent_NAS_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lu_PADA_Jointly_Sampling_Path_and_Data_for_Consistent_NAS_CVPR_2023_paper.html | CVPR 2023 | null |
Sphere-Guided Training of Neural Implicit Surfaces | Andreea Dogaru, Andrei-Timotei Ardelean, Savva Ignatyev, Egor Zakharov, Evgeny Burnaev | In recent years, neural distance functions trained via volumetric ray marching have been widely adopted for multi-view 3D reconstruction. These methods, however, apply the ray marching procedure for the entire scene volume, leading to reduced sampling efficiency and, as a result, lower reconstruction quality in the are... | https://openaccess.thecvf.com/content/CVPR2023/papers/Dogaru_Sphere-Guided_Training_of_Neural_Implicit_Surfaces_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Dogaru_Sphere-Guided_Training_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.15511 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Dogaru_Sphere-Guided_Training_of_Neural_Implicit_Surfaces_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Dogaru_Sphere-Guided_Training_of_Neural_Implicit_Surfaces_CVPR_2023_paper.html | CVPR 2023 | null |
3D Spatial Multimodal Knowledge Accumulation for Scene Graph Prediction in Point Cloud | Mingtao Feng, Haoran Hou, Liang Zhang, Zijie Wu, Yulan Guo, Ajmal Mian | In-depth understanding of a 3D scene not only involves locating/recognizing individual objects, but also requires to infer the relationships and interactions among them. However, since 3D scenes contain partially scanned objects with physical connections, dense placement, changing sizes, and a wide variety of challengi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_3D_Spatial_Multimodal_Knowledge_Accumulation_for_Scene_Graph_Prediction_in_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_3D_Spatial_Multimodal_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_3D_Spatial_Multimodal_Knowledge_Accumulation_for_Scene_Graph_Prediction_in_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_3D_Spatial_Multimodal_Knowledge_Accumulation_for_Scene_Graph_Prediction_in_CVPR_2023_paper.html | CVPR 2023 | null |
Extracting Motion and Appearance via Inter-Frame Attention for Efficient Video Frame Interpolation | Guozhen Zhang, Yuhan Zhu, Haonan Wang, Youxin Chen, Gangshan Wu, Limin Wang | Effectively extracting inter-frame motion and appearance information is important for video frame interpolation (VFI). Previous works either extract both types of information in a mixed way or devise separate modules for each type of information, which lead to representation ambiguity and low efficiency. In this paper,... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Extracting_Motion_and_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.00440 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Extracting_Motion_and_Appearance_via_Inter-Frame_Attention_for_Efficient_Video_CVPR_2023_paper.html | CVPR 2023 | null |
Bias Mimicking: A Simple Sampling Approach for Bias Mitigation | Maan Qraitem, Kate Saenko, Bryan A. Plummer | Prior work has shown that Visual Recognition datasets frequently underrepresent bias groups B (e.g. Female) within class labels Y (e.g. Programmers). This dataset bias can lead to models that learn spurious correlations between class labels and bias groups such as age, gender, or race. Most recent methods that address ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Qraitem_Bias_Mimicking_A_Simple_Sampling_Approach_for_Bias_Mitigation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qraitem_Bias_Mimicking_A_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2209.15605 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qraitem_Bias_Mimicking_A_Simple_Sampling_Approach_for_Bias_Mitigation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qraitem_Bias_Mimicking_A_Simple_Sampling_Approach_for_Bias_Mitigation_CVPR_2023_paper.html | CVPR 2023 | null |
ViTs for SITS: Vision Transformers for Satellite Image Time Series | Michail Tarasiou, Erik Chavez, Stefanos Zafeiriou | In this paper we introduce the Temporo-Spatial Vision Transformer (TSViT), a fully-attentional model for general Satellite Image Time Series (SITS) processing based on the Vision Transformer (ViT). TSViT splits a SITS record into non-overlapping patches in space and time which are tokenized and subsequently processed b... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tarasiou_ViTs_for_SITS_Vision_Transformers_for_Satellite_Image_Time_Series_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tarasiou_ViTs_for_SITS_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.04944 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tarasiou_ViTs_for_SITS_Vision_Transformers_for_Satellite_Image_Time_Series_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tarasiou_ViTs_for_SITS_Vision_Transformers_for_Satellite_Image_Time_Series_CVPR_2023_paper.html | CVPR 2023 | null |
NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers | Yijiang Liu, Huanrui Yang, Zhen Dong, Kurt Keutzer, Li Du, Shanghang Zhang | The complicated architecture and high training cost of vision transformers urge the exploration of post-training quantization. However, the heavy-tailed distribution of vision transformer activations hinders the effectiveness of previous post-training quantization methods, even with advanced quantizer designs. Instead ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_NoisyQuant_Noisy_Bias-Enhanced_Post-Training_Activation_Quantization_for_Vision_Transformers_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_NoisyQuant_Noisy_Bias-Enhanced_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.16056 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_NoisyQuant_Noisy_Bias-Enhanced_Post-Training_Activation_Quantization_for_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_NoisyQuant_Noisy_Bias-Enhanced_Post-Training_Activation_Quantization_for_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus | Wenhao Wu, Hau San Wong, Si Wu | Stereo-based 3D object detection, which aims at detecting 3D objects with stereo cameras, shows great potential in low-cost deployment compared to LiDAR-based methods and excellent performance compared to monocular-based algorithms. However, the impressive performance of stereo-based 3D object detection is at the huge ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Semi-Supervised_Stereo-Based_3D_Object_Detection_via_Cross-View_Consensus_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Semi-Supervised_Stereo-Based_3D_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Semi-Supervised_Stereo-Based_3D_Object_Detection_via_Cross-View_Consensus_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Semi-Supervised_Stereo-Based_3D_Object_Detection_via_Cross-View_Consensus_CVPR_2023_paper.html | CVPR 2023 | null |
Minimizing Maximum Model Discrepancy for Transferable Black-Box Targeted Attacks | Anqi Zhao, Tong Chu, Yahao Liu, Wen Li, Jingjing Li, Lixin Duan | In this work, we study the black-box targeted attack problem from the model discrepancy perspective. On the theoretical side, we present a generalization error bound for black-box targeted attacks, which gives a rigorous theoretical analysis for guaranteeing the success of the attack. We reveal that the attack error on... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Minimizing_Maximum_Model_Discrepancy_for_Transferable_Black-Box_Targeted_Attacks_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhao_Minimizing_Maximum_Model_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09035 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Minimizing_Maximum_Model_Discrepancy_for_Transferable_Black-Box_Targeted_Attacks_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Minimizing_Maximum_Model_Discrepancy_for_Transferable_Black-Box_Targeted_Attacks_CVPR_2023_paper.html | CVPR 2023 | null |
Efficient Loss Function by Minimizing the Detrimental Effect of Floating-Point Errors on Gradient-Based Attacks | Yunrui Yu, Cheng-Zhong Xu | Attackers can deceive neural networks by adding human imperceptive perturbations to their input data; this reveals the vulnerability and weak robustness of current deep-learning networks. Many attack techniques have been proposed to evaluate the model's robustness. Gradient-based attacks suffer from severely overestima... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Efficient_Loss_Function_by_Minimizing_the_Detrimental_Effect_of_Floating-Point_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yu_Efficient_Loss_Function_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Efficient_Loss_Function_by_Minimizing_the_Detrimental_Effect_of_Floating-Point_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_Efficient_Loss_Function_by_Minimizing_the_Detrimental_Effect_of_Floating-Point_CVPR_2023_paper.html | CVPR 2023 | null |
BAD-NeRF: Bundle Adjusted Deblur Neural Radiance Fields | Peng Wang, Lingzhe Zhao, Ruijie Ma, Peidong Liu | Neural Radiance Fields (NeRF) have received considerable attention recently, due to its impressive capability in photo-realistic 3D reconstruction and novel view synthesis, given a set of posed camera images. Earlier work usually assumes the input images are of good quality. However, image degradation (e.g. image motio... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_BAD-NeRF_Bundle_Adjusted_Deblur_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_BAD-NeRF_Bundle_Adjusted_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_BAD-NeRF_Bundle_Adjusted_Deblur_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_BAD-NeRF_Bundle_Adjusted_Deblur_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Video Compression With Entropy-Constrained Neural Representations | Carlos Gomes, Roberto Azevedo, Christopher Schroers | Encoding videos as neural networks is a recently proposed approach that allows new forms of video processing. However, traditional techniques still outperform such neural video representation (NVR) methods for the task of video compression. This performance gap can be explained by the fact that current NVR methods: i) ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Gomes_Video_Compression_With_Entropy-Constrained_Neural_Representations_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Gomes_Video_Compression_With_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Gomes_Video_Compression_With_Entropy-Constrained_Neural_Representations_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Gomes_Video_Compression_With_Entropy-Constrained_Neural_Representations_CVPR_2023_paper.html | CVPR 2023 | null |
Prompt, Generate, Then Cache: Cascade of Foundation Models Makes Strong Few-Shot Learners | Renrui Zhang, Xiangfei Hu, Bohao Li, Siyuan Huang, Hanqiu Deng, Yu Qiao, Peng Gao, Hongsheng Li | Visual recognition in low-data regimes requires deep neural networks to learn generalized representations from limited training samples. Recently, CLIP-based methods have shown promising few-shot performance benefited from the contrastive language-image pre-training. We then question, if the more diverse pre-training k... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Prompt_Generate_Then_Cache_Cascade_of_Foundation_Models_Makes_Strong_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Prompt_Generate_Then_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.02151 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Prompt_Generate_Then_Cache_Cascade_of_Foundation_Models_Makes_Strong_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Prompt_Generate_Then_Cache_Cascade_of_Foundation_Models_Makes_Strong_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Random Projector: Accelerated Deep Image Prior | Taihui Li, Hengkang Wang, Zhong Zhuang, Ju Sun | Deep image prior (DIP) has shown great promise in tackling a variety of image restoration (IR) and general visual inverse problems, needing no training data. However, the resulting optimization process is often very slow, inevitably hindering DIP's practical usage for time-sensitive scenarios. In this paper, we focus o... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Deep_Random_Projector_Accelerated_Deep_Image_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Deep_Random_Projector_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Deep_Random_Projector_Accelerated_Deep_Image_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Deep_Random_Projector_Accelerated_Deep_Image_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
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