GFPose: Learning 3D Human Pose Prior with Gradient Fields
Paper • 2212.08641 • Published
Search is not available for this dataset
title string | authors string | abstract string | pdf string | supp string | arXiv string | bibtex string | url string | detail_url string | tags string | string |
|---|---|---|---|---|---|---|---|---|---|---|
GFPose: Learning 3D Human Pose Prior With Gradient Fields | Hai Ci, Mingdong Wu, Wentao Zhu, Xiaoxuan Ma, Hao Dong, Fangwei Zhong, Yizhou Wang | Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the core of GFPose is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed 3D ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ci_GFPose_Learning_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.08641 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
CXTrack: Improving 3D Point Cloud Tracking With Contextual Information | Tian-Xing Xu, Yuan-Chen Guo, Yu-Kun Lai, Song-Hai Zhang | 3D single object tracking plays an essential role in many applications, such as autonomous driving. It remains a challenging problem due to the large appearance variation and the sparsity of points caused by occlusion and limited sensor capabilities. Therefore, contextual information across two consecutive frames is cr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_CXTrack_Improving_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.08542 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_CXTrack_Improving_3D_Point_Cloud_Tracking_With_Contextual_Information_CVPR_2023_paper.html | CVPR 2023 | null |
Deep Frequency Filtering for Domain Generalization | Shiqi Lin, Zhizheng Zhang, Zhipeng Huang, Yan Lu, Cuiling Lan, Peng Chu, Quanzeng You, Jiang Wang, Zicheng Liu, Amey Parulkar, Viraj Navkal, Zhibo Chen | Improving the generalization ability of Deep Neural Networks (DNNs) is critical for their practical uses, which has been a longstanding challenge. Some theoretical studies have uncovered that DNNs have preferences for some frequency components in the learning process and indicated that this may affect the robustness of... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Deep_Frequency_Filtering_for_Domain_Generalization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Deep_Frequency_Filtering_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2203.12198 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Deep_Frequency_Filtering_for_Domain_Generalization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Deep_Frequency_Filtering_for_Domain_Generalization_CVPR_2023_paper.html | CVPR 2023 | null |
Frame Flexible Network | Yitian Zhang, Yue Bai, Chang Liu, Huan Wang, Sheng Li, Yun Fu | Existing video recognition algorithms always conduct different training pipelines for inputs with different frame numbers, which requires repetitive training operations and multiplying storage costs. If we evaluate the model using other frames which are not used in training, we observe the performance will drop signifi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Frame_Flexible_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Frame_Flexible_Network_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14817 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Frame_Flexible_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Frame_Flexible_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow | Hanyu Zhou, Yi Chang, Wending Yan, Luxin Yan | Optical flow has achieved great success under clean scenes, but suffers from restricted performance under foggy scenes. To bridge the clean-to-foggy domain gap, the existing methods typically adopt the domain adaptation to transfer the motion knowledge from clean to synthetic foggy domain. However, these methods unexpe... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhou_Unsupervised_Cumulative_Domain_Adaptation_for_Foggy_Scene_Optical_Flow_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhou_Unsupervised_Cumulative_Domain_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2303.07564 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Unsupervised_Cumulative_Domain_Adaptation_for_Foggy_Scene_Optical_Flow_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhou_Unsupervised_Cumulative_Domain_Adaptation_for_Foggy_Scene_Optical_Flow_CVPR_2023_paper.html | CVPR 2023 | null |
NoisyTwins: Class-Consistent and Diverse Image Generation Through StyleGANs | Harsh Rangwani, Lavish Bansal, Kartik Sharma, Tejan Karmali, Varun Jampani, R. Venkatesh Babu | StyleGANs are at the forefront of controllable image generation as they produce a latent space that is semantically disentangled, making it suitable for image editing and manipulation. However, the performance of StyleGANs severely degrades when trained via class-conditioning on large-scale long-tailed datasets. We fin... | https://openaccess.thecvf.com/content/CVPR2023/papers/Rangwani_NoisyTwins_Class-Consistent_and_Diverse_Image_Generation_Through_StyleGANs_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Rangwani_NoisyTwins_Class-Consistent_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05866 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Rangwani_NoisyTwins_Class-Consistent_and_Diverse_Image_Generation_Through_StyleGANs_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Rangwani_NoisyTwins_Class-Consistent_and_Diverse_Image_Generation_Through_StyleGANs_CVPR_2023_paper.html | CVPR 2023 | null |
DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-Aware Scene Synthesis | Yinghao Xu, Menglei Chai, Zifan Shi, Sida Peng, Ivan Skorokhodov, Aliaksandr Siarohin, Ceyuan Yang, Yujun Shen, Hsin-Ying Lee, Bolei Zhou, Sergey Tulyakov | Existing 3D-aware image synthesis approaches mainly focus on generating a single canonical object and show limited capacity in composing a complex scene containing a variety of objects. This work presents DisCoScene: a 3D-aware generative model for high-quality and controllable scene synthesis. The key ingredient of ou... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_DisCoScene_Spatially_Disentangled_Generative_Radiance_Fields_for_Controllable_3D-Aware_Scene_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_DisCoScene_Spatially_Disentangled_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.11984 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_DisCoScene_Spatially_Disentangled_Generative_Radiance_Fields_for_Controllable_3D-Aware_Scene_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_DisCoScene_Spatially_Disentangled_Generative_Radiance_Fields_for_Controllable_3D-Aware_Scene_CVPR_2023_paper.html | CVPR 2023 | null |
Revisiting Self-Similarity: Structural Embedding for Image Retrieval | Seongwon Lee, Suhyeon Lee, Hongje Seong, Euntai Kim | Despite advances in global image representation, existing image retrieval approaches rarely consider geometric structure during the global retrieval stage. In this work, we revisit the conventional self-similarity descriptor from a convolutional perspective, to encode both the visual and structural cues of the image to... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Revisiting_Self-Similarity_Structural_Embedding_for_Image_Retrieval_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Revisiting_Self-Similarity_Structural_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Revisiting_Self-Similarity_Structural_Embedding_for_Image_Retrieval_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Revisiting_Self-Similarity_Structural_Embedding_for_Image_Retrieval_CVPR_2023_paper.html | CVPR 2023 | null |
Minimizing the Accumulated Trajectory Error To Improve Dataset Distillation | Jiawei Du, Yidi Jiang, Vincent Y. F. Tan, Joey Tianyi Zhou, Haizhou Li | Model-based deep learning has achieved astounding successes due in part to the availability of large-scale real-world data. However, processing such massive amounts of data comes at a considerable cost in terms of computations, storage, training and the search for good neural architectures. Dataset distillation has thu... | https://openaccess.thecvf.com/content/CVPR2023/papers/Du_Minimizing_the_Accumulated_Trajectory_Error_To_Improve_Dataset_Distillation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Du_Minimizing_the_Accumulated_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.11004 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Minimizing_the_Accumulated_Trajectory_Error_To_Improve_Dataset_Distillation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Du_Minimizing_the_Accumulated_Trajectory_Error_To_Improve_Dataset_Distillation_CVPR_2023_paper.html | CVPR 2023 | null |
Decoupling-and-Aggregating for Image Exposure Correction | Yang Wang, Long Peng, Liang Li, Yang Cao, Zheng-Jun Zha | The images captured under improper exposure conditions often suffer from contrast degradation and detail distortion. Contrast degradation will destroy the statistical properties of low-frequency components, while detail distortion will disturb the structural properties of high-frequency components, leading to the low-f... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Decoupling-and-Aggregating_for_Image_Exposure_Correction_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Decoupling-and-Aggregating_for_Image_Exposure_Correction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Decoupling-and-Aggregating_for_Image_Exposure_Correction_CVPR_2023_paper.html | CVPR 2023 | null |
Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving | Ben Agro, Quinlan Sykora, Sergio Casas, Raquel Urtasun | A self-driving vehicle (SDV) must be able to perceive its surroundings and predict the future behavior of other traffic participants. Existing works either perform object detection followed by trajectory forecasting of the detected objects, or predict dense occupancy and flow grids for the whole scene. The former poses... | https://openaccess.thecvf.com/content/CVPR2023/papers/Agro_Implicit_Occupancy_Flow_Fields_for_Perception_and_Prediction_in_Self-Driving_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Agro_Implicit_Occupancy_Flow_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Agro_Implicit_Occupancy_Flow_Fields_for_Perception_and_Prediction_in_Self-Driving_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Agro_Implicit_Occupancy_Flow_Fields_for_Perception_and_Prediction_in_Self-Driving_CVPR_2023_paper.html | CVPR 2023 | null |
CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes | Harshil Bhatia, Edith Tretschk, Zorah Lähner, Marcel Seelbach Benkner, Michael Moeller, Christian Theobalt, Vladislav Golyanik | Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, NP-hard problem. A perfect matching is necessarily cycle-consistent: Following the pairwise point correspondences along several shapes must end up at the starting vertex of the original shape. Unfortunately, existing quantum shape-matching meth... | https://openaccess.thecvf.com/content/CVPR2023/papers/Bhatia_CCuantuMM_Cycle-Consistent_Quantum-Hybrid_Matching_of_Multiple_Shapes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bhatia_CCuantuMM_Cycle-Consistent_Quantum-Hybrid_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bhatia_CCuantuMM_Cycle-Consistent_Quantum-Hybrid_Matching_of_Multiple_Shapes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bhatia_CCuantuMM_Cycle-Consistent_Quantum-Hybrid_Matching_of_Multiple_Shapes_CVPR_2023_paper.html | CVPR 2023 | null |
TrojViT: Trojan Insertion in Vision Transformers | Mengxin Zheng, Qian Lou, Lei Jiang | Vision Transformers (ViTs) have demonstrated the state-of-the-art performance in various vision-related tasks. The success of ViTs motivates adversaries to perform backdoor attacks on ViTs. Although the vulnerability of traditional CNNs to backdoor attacks is well-known, backdoor attacks on ViTs are seldom-studied. Com... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zheng_TrojViT_Trojan_Insertion_in_Vision_Transformers_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2208.13049 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_TrojViT_Trojan_Insertion_in_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zheng_TrojViT_Trojan_Insertion_in_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
MarS3D: A Plug-and-Play Motion-Aware Model for Semantic Segmentation on Multi-Scan 3D Point Clouds | Jiahui Liu, Chirui Chang, Jianhui Liu, Xiaoyang Wu, Lan Ma, Xiaojuan Qi | 3D semantic segmentation on multi-scan large-scale point clouds plays an important role in autonomous systems. Unlike the single-scan-based semantic segmentation task, this task requires distinguishing the motion states of points in addition to their semantic categories. However, methods designed for single-scan-based ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_MarS3D_A_Plug-and-Play_Motion-Aware_Model_for_Semantic_Segmentation_on_Multi-Scan_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_MarS3D_A_Plug-and-Play_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_MarS3D_A_Plug-and-Play_Motion-Aware_Model_for_Semantic_Segmentation_on_Multi-Scan_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_MarS3D_A_Plug-and-Play_Motion-Aware_Model_for_Semantic_Segmentation_on_Multi-Scan_CVPR_2023_paper.html | CVPR 2023 | null |
An Image Quality Assessment Dataset for Portraits | Nicolas Chahine, Stefania Calarasanu, Davide Garcia-Civiero, Théo Cayla, Sira Ferradans, Jean Ponce | Year after year, the demand for ever-better smartphone photos continues to grow, in particular in the domain of portrait photography. Manufacturers thus use perceptual quality criteria throughout the development of smartphone cameras. This costly procedure can be partially replaced by automated learning-based methods f... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chahine_An_Image_Quality_Assessment_Dataset_for_Portraits_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chahine_An_Image_Quality_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05772 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chahine_An_Image_Quality_Assessment_Dataset_for_Portraits_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chahine_An_Image_Quality_Assessment_Dataset_for_Portraits_CVPR_2023_paper.html | CVPR 2023 | null |
MSeg3D: Multi-Modal 3D Semantic Segmentation for Autonomous Driving | Jiale Li, Hang Dai, Hao Han, Yong Ding | LiDAR and camera are two modalities available for 3D semantic segmentation in autonomous driving. The popular LiDAR-only methods severely suffer from inferior segmentation on small and distant objects due to insufficient laser points, while the robust multi-modal solution is under-explored, where we investigate three c... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_MSeg3D_Multi-Modal_3D_Semantic_Segmentation_for_Autonomous_Driving_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_MSeg3D_Multi-Modal_3D_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.08600 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MSeg3D_Multi-Modal_3D_Semantic_Segmentation_for_Autonomous_Driving_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_MSeg3D_Multi-Modal_3D_Semantic_Segmentation_for_Autonomous_Driving_CVPR_2023_paper.html | CVPR 2023 | null |
Robust Outlier Rejection for 3D Registration With Variational Bayes | Haobo Jiang, Zheng Dang, Zhen Wei, Jin Xie, Jian Yang, Mathieu Salzmann | Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The core for this to be successful is to learn the discriminative inlier/outlier feature representations. In this paper, we develop a novel variat... | https://openaccess.thecvf.com/content/CVPR2023/papers/Jiang_Robust_Outlier_Rejection_for_3D_Registration_With_Variational_Bayes_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Jiang_Robust_Outlier_Rejection_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01514 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Robust_Outlier_Rejection_for_3D_Registration_With_Variational_Bayes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Jiang_Robust_Outlier_Rejection_for_3D_Registration_With_Variational_Bayes_CVPR_2023_paper.html | CVPR 2023 | null |
Dynamically Instance-Guided Adaptation: A Backward-Free Approach for Test-Time Domain Adaptive Semantic Segmentation | Wei Wang, Zhun Zhong, Weijie Wang, Xi Chen, Charles Ling, Boyu Wang, Nicu Sebe | In this paper, we study the application of Test-time domain adaptation in semantic segmentation (TTDA-Seg) where both efficiency and effectiveness are crucial. Existing methods either have low efficiency (e.g., backward optimization) or ignore semantic adaptation (e.g., distribution alignment). Besides, they would suff... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_Dynamically_Instance-Guided_Adaptation_A_Backward-Free_Approach_for_Test-Time_Domain_Adaptive_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wang_Dynamically_Instance-Guided_Adaptation_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Dynamically_Instance-Guided_Adaptation_A_Backward-Free_Approach_for_Test-Time_Domain_Adaptive_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_Dynamically_Instance-Guided_Adaptation_A_Backward-Free_Approach_for_Test-Time_Domain_Adaptive_CVPR_2023_paper.html | CVPR 2023 | null |
Painting 3D Nature in 2D: View Synthesis of Natural Scenes From a Single Semantic Mask | Shangzhan Zhang, Sida Peng, Tianrun Chen, Linzhan Mou, Haotong Lin, Kaicheng Yu, Yiyi Liao, Xiaowei Zhou | We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet. Prior works on 3D-aware image synthesis either require multi-view supervision or learning category-level prior for sp... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Painting_3D_Nature_in_2D_View_Synthesis_of_Natural_Scenes_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2302.07224 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Painting_3D_Nature_in_2D_View_Synthesis_of_Natural_Scenes_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Painting_3D_Nature_in_2D_View_Synthesis_of_Natural_Scenes_CVPR_2023_paper.html | CVPR 2023 | null |
LANIT: Language-Driven Image-to-Image Translation for Unlabeled Data | Jihye Park, Sunwoo Kim, Soohyun Kim, Seokju Cho, Jaejun Yoo, Youngjung Uh, Seungryong Kim | Existing techniques for image-to-image translation commonly have suffered from two critical problems: heavy reliance on per-sample domain annotation and/or inability to handle multiple attributes per image. Recent truly-unsupervised methods adopt clustering approaches to easily provide per-sample one-hot domain labels.... | https://openaccess.thecvf.com/content/CVPR2023/papers/Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Park_LANIT_Language-Driven_Image-to-Image_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2208.14889 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Park_LANIT_Language-Driven_Image-to-Image_Translation_for_Unlabeled_Data_CVPR_2023_paper.html | CVPR 2023 | null |
MoLo: Motion-Augmented Long-Short Contrastive Learning for Few-Shot Action Recognition | Xiang Wang, Shiwei Zhang, Zhiwu Qing, Changxin Gao, Yingya Zhang, Deli Zhao, Nong Sang | Current state-of-the-art approaches for few-shot action recognition achieve promising performance by conducting frame-level matching on learned visual features. However, they generally suffer from two limitations: i) the matching procedure between local frames tends to be inaccurate due to the lack of guidance to force... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wang_MoLo_Motion-Augmented_Long-Short_Contrastive_Learning_for_Few-Shot_Action_Recognition_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.00946 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MoLo_Motion-Augmented_Long-Short_Contrastive_Learning_for_Few-Shot_Action_Recognition_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wang_MoLo_Motion-Augmented_Long-Short_Contrastive_Learning_for_Few-Shot_Action_Recognition_CVPR_2023_paper.html | CVPR 2023 | null |
Fast Point Cloud Generation With Straight Flows | Lemeng Wu, Dilin Wang, Chengyue Gong, Xingchao Liu, Yunyang Xiong, Rakesh Ranjan, Raghuraman Krishnamoorthi, Vikas Chandra, Qiang Liu | Diffusion models have emerged as a powerful tool for point cloud generation. A key component that drives the impressive performance for generating high-quality samples from noise is iteratively denoise for thousands of steps. While beneficial, the complexity of learning steps has limited its applications to many 3D rea... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Fast_Point_Cloud_Generation_With_Straight_Flows_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Fast_Point_Cloud_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2212.01747 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Fast_Point_Cloud_Generation_With_Straight_Flows_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Fast_Point_Cloud_Generation_With_Straight_Flows_CVPR_2023_paper.html | CVPR 2023 | null |
Text-Guided Unsupervised Latent Transformation for Multi-Attribute Image Manipulation | Xiwen Wei, Zhen Xu, Cheng Liu, Si Wu, Zhiwen Yu, Hau San Wong | Great progress has been made in StyleGAN-based image editing. To associate with preset attributes, most existing approaches focus on supervised learning for semantically meaningful latent space traversal directions, and each manipulation step is typically determined for an individual attribute. To address this limitati... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wei_Text-Guided_Unsupervised_Latent_Transformation_for_Multi-Attribute_Image_Manipulation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Text-Guided_Unsupervised_Latent_Transformation_for_Multi-Attribute_Image_Manipulation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wei_Text-Guided_Unsupervised_Latent_Transformation_for_Multi-Attribute_Image_Manipulation_CVPR_2023_paper.html | CVPR 2023 | null |
Achieving a Better Stability-Plasticity Trade-Off via Auxiliary Networks in Continual Learning | Sanghwan Kim, Lorenzo Noci, Antonio Orvieto, Thomas Hofmann | In contrast to the natural capabilities of humans to learn new tasks in a sequential fashion, neural networks are known to suffer from catastrophic forgetting, where the model's performances on old tasks drop dramatically after being optimized for a new task. Since then, the continual learning (CL) community has propos... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kim_Achieving_a_Better_Stability-Plasticity_Trade-Off_via_Auxiliary_Networks_in_Continual_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kim_Achieving_a_Better_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09483 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Achieving_a_Better_Stability-Plasticity_Trade-Off_via_Auxiliary_Networks_in_Continual_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kim_Achieving_a_Better_Stability-Plasticity_Trade-Off_via_Auxiliary_Networks_in_Continual_CVPR_2023_paper.html | CVPR 2023 | null |
Power Bundle Adjustment for Large-Scale 3D Reconstruction | Simon Weber, Nikolaus Demmel, Tin Chon Chan, Daniel Cremers | We introduce Power Bundle Adjustment as an expansion type algorithm for solving large-scale bundle adjustment problems. It is based on the power series expansion of the inverse Schur complement and constitutes a new family of solvers that we call inverse expansion methods. We theoretically justify the use of power seri... | https://openaccess.thecvf.com/content/CVPR2023/papers/Weber_Power_Bundle_Adjustment_for_Large-Scale_3D_Reconstruction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Weber_Power_Bundle_Adjustment_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2204.12834 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Weber_Power_Bundle_Adjustment_for_Large-Scale_3D_Reconstruction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Weber_Power_Bundle_Adjustment_for_Large-Scale_3D_Reconstruction_CVPR_2023_paper.html | CVPR 2023 | null |
Picture That Sketch: Photorealistic Image Generation From Abstract Sketches | Subhadeep Koley, Ayan Kumar Bhunia, Aneeshan Sain, Pinaki Nath Chowdhury, Tao Xiang, Yi-Zhe Song | Given an abstract, deformed, ordinary sketch from untrained amateurs like you and me, this paper turns it into a photorealistic image - just like those shown in Fig. 1(a), all non-cherry-picked. We differ significantly from prior art in that we do not dictate an edgemap-like sketch to start with, but aim to work with a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Koley_Picture_That_Sketch_Photorealistic_Image_Generation_From_Abstract_Sketches_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Koley_Picture_That_Sketch_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.11162 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Koley_Picture_That_Sketch_Photorealistic_Image_Generation_From_Abstract_Sketches_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Koley_Picture_That_Sketch_Photorealistic_Image_Generation_From_Abstract_Sketches_CVPR_2023_paper.html | CVPR 2023 | null |
Contrastive Semi-Supervised Learning for Underwater Image Restoration via Reliable Bank | Shirui Huang, Keyan Wang, Huan Liu, Jun Chen, Yunsong Li | Despite the remarkable achievement of recent underwater image restoration techniques, the lack of labeled data has become a major hurdle for further progress. In this work, we propose a mean-teacher based Semi-supervised Underwater Image Restoration (Semi-UIR) framework to incorporate the unlabeled data into network tr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Contrastive_Semi-Supervised_Learning_for_Underwater_Image_Restoration_via_Reliable_Bank_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Contrastive_Semi-Supervised_Learning_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.09101 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Contrastive_Semi-Supervised_Learning_for_Underwater_Image_Restoration_via_Reliable_Bank_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Contrastive_Semi-Supervised_Learning_for_Underwater_Image_Restoration_via_Reliable_Bank_CVPR_2023_paper.html | CVPR 2023 | null |
Video Event Restoration Based on Keyframes for Video Anomaly Detection | Zhiwei Yang, Jing Liu, Zhaoyang Wu, Peng Wu, Xiaotao Liu | Video anomaly detection (VAD) is a significant computer vision problem. Existing deep neural network (DNN) based VAD methods mostly follow the route of frame reconstruction or frame prediction. However, the lack of mining and learning of higher-level visual features and temporal context relationships in videos limits t... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_Video_Event_Restoration_Based_on_Keyframes_for_Video_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yang_Video_Event_Restoration_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05112 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Video_Event_Restoration_Based_on_Keyframes_for_Video_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_Video_Event_Restoration_Based_on_Keyframes_for_Video_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
EcoTTA: Memory-Efficient Continual Test-Time Adaptation via Self-Distilled Regularization | Junha Song, Jungsoo Lee, In So Kweon, Sungha Choi | This paper presents a simple yet effective approach that improves continual test-time adaptation (TTA) in a memory-efficient manner. TTA may primarily be conducted on edge devices with limited memory, so reducing memory is crucial but has been overlooked in previous TTA studies. In addition, long-term adaptation often ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Song_EcoTTA_Memory-Efficient_Continual_Test-Time_Adaptation_via_Self-Distilled_Regularization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Song_EcoTTA_Memory-Efficient_Continual_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.01904 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Song_EcoTTA_Memory-Efficient_Continual_Test-Time_Adaptation_via_Self-Distilled_Regularization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Song_EcoTTA_Memory-Efficient_Continual_Test-Time_Adaptation_via_Self-Distilled_Regularization_CVPR_2023_paper.html | CVPR 2023 | null |
3D-Aware Object Goal Navigation via Simultaneous Exploration and Identification | Jiazhao Zhang, Liu Dai, Fanpeng Meng, Qingnan Fan, Xuelin Chen, Kai Xu, He Wang | Object goal navigation (ObjectNav) in unseen environments is a fundamental task for Embodied AI. Agents in existing works learn ObjectNav policies based on 2D maps, scene graphs, or image sequences. Considering this task happens in 3D space, a 3D-aware agent can advance its ObjectNav capability via learning from fine-g... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_3D-Aware_Object_Goal_Navigation_via_Simultaneous_Exploration_and_Identification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_3D-Aware_Object_Goal_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.00338 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_3D-Aware_Object_Goal_Navigation_via_Simultaneous_Exploration_and_Identification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_3D-Aware_Object_Goal_Navigation_via_Simultaneous_Exploration_and_Identification_CVPR_2023_paper.html | CVPR 2023 | null |
Tri-Perspective View for Vision-Based 3D Semantic Occupancy Prediction | Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, Jiwen Lu | Modern methods for vision-centric autonomous driving perception widely adopt the bird's-eye-view (BEV) representation to describe a 3D scene. Despite its better efficiency than voxel representation, it has difficulty describing the fine-grained 3D structure of a scene with a single plane. To address this, we propose a ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Tri-Perspective_View_for_Vision-Based_3D_Semantic_Occupancy_Prediction_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Huang_Tri-Perspective_View_for_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.07817 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Tri-Perspective_View_for_Vision-Based_3D_Semantic_Occupancy_Prediction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Tri-Perspective_View_for_Vision-Based_3D_Semantic_Occupancy_Prediction_CVPR_2023_paper.html | CVPR 2023 | null |
Castling-ViT: Compressing Self-Attention via Switching Towards Linear-Angular Attention at Vision Transformer Inference | Haoran You, Yunyang Xiong, Xiaoliang Dai, Bichen Wu, Peizhao Zhang, Haoqi Fan, Peter Vajda, Yingyan (Celine) Lin | Vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs' attention measures global similarities and thus has a quadratic complexity with the number of input tokens. Existing efficient ViTs adopt l... | https://openaccess.thecvf.com/content/CVPR2023/papers/You_Castling-ViT_Compressing_Self-Attention_via_Switching_Towards_Linear-Angular_Attention_at_Vision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/You_Castling-ViT_Compressing_Self-Attention_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/You_Castling-ViT_Compressing_Self-Attention_via_Switching_Towards_Linear-Angular_Attention_at_Vision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/You_Castling-ViT_Compressing_Self-Attention_via_Switching_Towards_Linear-Angular_Attention_at_Vision_CVPR_2023_paper.html | CVPR 2023 | null |
Shape, Pose, and Appearance From a Single Image via Bootstrapped Radiance Field Inversion | Dario Pavllo, David Joseph Tan, Marie-Julie Rakotosaona, Federico Tombari | Neural Radiance Fields (NeRF) coupled with GANs represent a promising direction in the area of 3D reconstruction from a single view, owing to their ability to efficiently model arbitrary topologies. Recent work in this area, however, has mostly focused on synthetic datasets where exact ground-truth poses are known, and... | https://openaccess.thecvf.com/content/CVPR2023/papers/Pavllo_Shape_Pose_and_Appearance_From_a_Single_Image_via_Bootstrapped_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pavllo_Shape_Pose_and_CVPR_2023_supplemental.zip | http://arxiv.org/abs/2211.11674 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pavllo_Shape_Pose_and_Appearance_From_a_Single_Image_via_Bootstrapped_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pavllo_Shape_Pose_and_Appearance_From_a_Single_Image_via_Bootstrapped_CVPR_2023_paper.html | CVPR 2023 | null |
Unlearnable Clusters: Towards Label-Agnostic Unlearnable Examples | Jiaming Zhang, Xingjun Ma, Qi Yi, Jitao Sang, Yu-Gang Jiang, Yaowei Wang, Changsheng Xu | There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimizati... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Unlearnable_Clusters_Towards_Label-Agnostic_Unlearnable_Examples_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.01217 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Unlearnable_Clusters_Towards_Label-Agnostic_Unlearnable_Examples_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Unlearnable_Clusters_Towards_Label-Agnostic_Unlearnable_Examples_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Federated Learning With Domain Shift: A Prototype View | Wenke Huang, Mang Ye, Zekun Shi, He Li, Bo Du | Federated learning shows a bright promise as a privacy-preserving collaborative learning technique. However, prevalent solutions mainly focus on all private data sampled from the same domain. An important challenge is that when distributed data are derived from diverse domains. The private model presents degenerative p... | https://openaccess.thecvf.com/content/CVPR2023/papers/Huang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Huang_Rethinking_Federated_Learning_With_Domain_Shift_A_Prototype_View_CVPR_2023_paper.html | CVPR 2023 | null |
NoPe-NeRF: Optimising Neural Radiance Field With No Pose Prior | Wenjing Bian, Zirui Wang, Kejie Li, Jia-Wang Bian, Victor Adrian Prisacariu | Training a Neural Radiance Field (NeRF) without pre-computed camera poses is challenging. Recent advances in this direction demonstrate the possibility of jointly optimising a NeRF and camera poses in forward-facing scenes. However, these methods still face difficulties during dramatic camera movement. We tackle this c... | https://openaccess.thecvf.com/content/CVPR2023/papers/Bian_NoPe-NeRF_Optimising_Neural_Radiance_Field_With_No_Pose_Prior_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bian_NoPe-NeRF_Optimising_Neural_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bian_NoPe-NeRF_Optimising_Neural_Radiance_Field_With_No_Pose_Prior_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bian_NoPe-NeRF_Optimising_Neural_Radiance_Field_With_No_Pose_Prior_CVPR_2023_paper.html | CVPR 2023 | null |
HGFormer: Hierarchical Grouping Transformer for Domain Generalized Semantic Segmentation | Jian Ding, Nan Xue, Gui-Song Xia, Bernt Schiele, Dengxin Dai | Current semantic segmentation models have achieved great success under the independent and identically distributed (i.i.d.) condition. However, in real-world applications, test data might come from a different domain than training data. Therefore, it is important to improve model robustness against domain differences. ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_HGFormer_Hierarchical_Grouping_Transformer_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_HGFormer_Hierarchical_Grouping_Transformer_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_HGFormer_Hierarchical_Grouping_Transformer_for_Domain_Generalized_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Distilling Vision-Language Pre-Training To Collaborate With Weakly-Supervised Temporal Action Localization | Chen Ju, Kunhao Zheng, Jinxiang Liu, Peisen Zhao, Ya Zhang, Jianlong Chang, Qi Tian, Yanfeng Wang | Weakly-supervised temporal action localization (WTAL) learns to detect and classify action instances with only category labels. Most methods widely adopt the off-the-shelf Classification-Based Pre-training (CBP) to generate video features for action localization. However, the different optimization objectives between c... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ju_Distilling_Vision-Language_Pre-Training_To_Collaborate_With_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ju_Distilling_Vision-Language_Pre-Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2212.09335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ju_Distilling_Vision-Language_Pre-Training_To_Collaborate_With_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ju_Distilling_Vision-Language_Pre-Training_To_Collaborate_With_Weakly-Supervised_Temporal_Action_Localization_CVPR_2023_paper.html | CVPR 2023 | null |
Augmentation Matters: A Simple-Yet-Effective Approach to Semi-Supervised Semantic Segmentation | Zhen Zhao, Lihe Yang, Sifan Long, Jimin Pi, Luping Zhou, Jingdong Wang | Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and additional training procedures. Differently, in this work, we follow a ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Augmentation_Matters_A_Simple-Yet-Effective_Approach_to_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2212.04976 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Augmentation_Matters_A_Simple-Yet-Effective_Approach_to_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Augmentation_Matters_A_Simple-Yet-Effective_Approach_to_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
SIEDOB: Semantic Image Editing by Disentangling Object and Background | Wuyang Luo, Su Yang, Xinjian Zhang, Weishan Zhang | Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects and the backgrounds are quite different. However, all previous methods handle backgrounds and objects as a whole using a monolithic model. C... | https://openaccess.thecvf.com/content/CVPR2023/papers/Luo_SIEDOB_Semantic_Image_Editing_by_Disentangling_Object_and_Background_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.13062 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_SIEDOB_Semantic_Image_Editing_by_Disentangling_Object_and_Background_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Luo_SIEDOB_Semantic_Image_Editing_by_Disentangling_Object_and_Background_CVPR_2023_paper.html | CVPR 2023 | null |
Multiclass Confidence and Localization Calibration for Object Detection | Bimsara Pathiraja, Malitha Gunawardhana, Muhammad Haris Khan | Albeit achieving high predictive accuracy across many challenging computer vision problems, recent studies suggest that deep neural networks (DNNs) tend to make overconfident predictions, rendering them poorly calibrated. Most of the existing attempts for improving DNN calibration are limited to classification tasks an... | https://openaccess.thecvf.com/content/CVPR2023/papers/Pathiraja_Multiclass_Confidence_and_Localization_Calibration_for_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Pathiraja_Multiclass_Confidence_and_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Pathiraja_Multiclass_Confidence_and_Localization_Calibration_for_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Pathiraja_Multiclass_Confidence_and_Localization_Calibration_for_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Query-Dependent Video Representation for Moment Retrieval and Highlight Detection | WonJun Moon, Sangeek Hyun, SangUk Park, Dongchan Park, Jae-Pil Heo | Recently, video moment retrieval and highlight detection (MR/HD) are being spotlighted as the demand for video understanding is drastically increased. The key objective of MR/HD is to localize the moment and estimate clip-wise accordance level, i.e., saliency score, to the given text query. Although the recent transfor... | https://openaccess.thecvf.com/content/CVPR2023/papers/Moon_Query-Dependent_Video_Representation_for_Moment_Retrieval_and_Highlight_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Moon_Query-Dependent_Video_Representation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13874 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Moon_Query-Dependent_Video_Representation_for_Moment_Retrieval_and_Highlight_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Moon_Query-Dependent_Video_Representation_for_Moment_Retrieval_and_Highlight_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Robust 3D Shape Classification via Non-Local Graph Attention Network | Shengwei Qin, Zhong Li, Ligang Liu | We introduce a non-local graph attention network (NLGAT), which generates a novel global descriptor through two sub-networks for robust 3D shape classification. In the first sub-network, we capture the global relationships between points (i.e., point-point features) by designing a global relationship network (GRN). In ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Qin_Robust_3D_Shape_Classification_via_Non-Local_Graph_Attention_Network_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Qin_Robust_3D_Shape_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Robust_3D_Shape_Classification_via_Non-Local_Graph_Attention_Network_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Qin_Robust_3D_Shape_Classification_via_Non-Local_Graph_Attention_Network_CVPR_2023_paper.html | CVPR 2023 | null |
Boosting Verified Training for Robust Image Classifications via Abstraction | Zhaodi Zhang, Zhiyi Xue, Yang Chen, Si Liu, Yueling Zhang, Jing Liu, Min Zhang | This paper proposes a novel, abstraction-based, certified training method for robust image classifiers. Via abstraction, all perturbed images are mapped into intervals before feeding into neural networks for training. By training on intervals, all the perturbed images that are mapped to the same interval are classified... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Boosting_Verified_Training_for_Robust_Image_Classifications_via_Abstraction_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.11552 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Boosting_Verified_Training_for_Robust_Image_Classifications_via_Abstraction_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Boosting_Verified_Training_for_Robust_Image_Classifications_via_Abstraction_CVPR_2023_paper.html | CVPR 2023 | null |
Exploring Structured Semantic Prior for Multi Label Recognition With Incomplete Labels | Zixuan Ding, Ao Wang, Hui Chen, Qiang Zhang, Pengzhang Liu, Yongjun Bao, Weipeng Yan, Jungong Han | Multi-label recognition (MLR) with incomplete labels is very challenging. Recent works strive to explore the image-to-label correspondence in the vision-language model, i.e., CLIP, to compensate for insufficient annotations. In spite of promising performance, they generally overlook the valuable prior about the label-t... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ding_Exploring_Structured_Semantic_Prior_for_Multi_Label_Recognition_With_Incomplete_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ding_Exploring_Structured_Semantic_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13223 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Exploring_Structured_Semantic_Prior_for_Multi_Label_Recognition_With_Incomplete_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ding_Exploring_Structured_Semantic_Prior_for_Multi_Label_Recognition_With_Incomplete_CVPR_2023_paper.html | CVPR 2023 | null |
Instance-Specific and Model-Adaptive Supervision for Semi-Supervised Semantic Segmentation | Zhen Zhao, Sifan Long, Jimin Pi, Jingdong Wang, Luping Zhou | Recently, semi-supervised semantic segmentation has achieved promising performance with a small fraction of labeled data. However, most existing studies treat all unlabeled data equally and barely consider the differences and training difficulties among unlabeled instances. Differentiating unlabeled instances can promo... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhao_Instance-Specific_and_Model-Adaptive_Supervision_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2211.11335 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Instance-Specific_and_Model-Adaptive_Supervision_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhao_Instance-Specific_and_Model-Adaptive_Supervision_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
3D Shape Reconstruction of Semi-Transparent Worms | Thomas P. Ilett, Omer Yuval, Thomas Ranner, Netta Cohen, David C. Hogg | 3D shape reconstruction typically requires identifying object features or textures in multiple images of a subject. This approach is not viable when the subject is semi-transparent and moving in and out of focus. Here we overcome these challenges by rendering a candidate shape with adaptive blurring and transparency fo... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ilett_3D_Shape_Reconstruction_of_Semi-Transparent_Worms_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ilett_3D_Shape_Reconstruction_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.14841 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ilett_3D_Shape_Reconstruction_of_Semi-Transparent_Worms_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ilett_3D_Shape_Reconstruction_of_Semi-Transparent_Worms_CVPR_2023_paper.html | CVPR 2023 | null |
Mapping Degeneration Meets Label Evolution: Learning Infrared Small Target Detection With Single Point Supervision | Xinyi Ying, Li Liu, Yingqian Wang, Ruojing Li, Nuo Chen, Zaiping Lin, Weidong Sheng, Shilin Zhou | Training a convolutional neural network (CNN) to detect infrared small targets in a fully supervised manner has gained remarkable research interests in recent years, but is highly labor expensive since a large number of per-pixel annotations are required. To handle this problem, in this paper, we make the first attempt... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ying_Mapping_Degeneration_Meets_Label_Evolution_Learning_Infrared_Small_Target_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ying_Mapping_Degeneration_Meets_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.01484 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ying_Mapping_Degeneration_Meets_Label_Evolution_Learning_Infrared_Small_Target_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ying_Mapping_Degeneration_Meets_Label_Evolution_Learning_Infrared_Small_Target_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Swept-Angle Synthetic Wavelength Interferometry | Alankar Kotwal, Anat Levin, Ioannis Gkioulekas | We present a new imaging technique, swept-angle synthetic wavelength interferometry, for full-field micron-scale 3D sensing. As in conventional synthetic wavelength interferometry, our technique uses light consisting of two narrowly-separated optical wavelengths, resulting in per-pixel interferometric measurements whos... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kotwal_Swept-Angle_Synthetic_Wavelength_Interferometry_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Kotwal_Swept-Angle_Synthetic_Wavelength_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.10655 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kotwal_Swept-Angle_Synthetic_Wavelength_Interferometry_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kotwal_Swept-Angle_Synthetic_Wavelength_Interferometry_CVPR_2023_paper.html | CVPR 2023 | null |
Delving Into Shape-Aware Zero-Shot Semantic Segmentation | Xinyu Liu, Beiwen Tian, Zhen Wang, Rui Wang, Kehua Sheng, Bo Zhang, Hao Zhao, Guyue Zhou | Thanks to the impressive progress of large-scale vision-language pretraining, recent recognition models can classify arbitrary objects in a zero-shot and open-set manner, with a surprisingly high accuracy. However, translating this success to semantic segmentation is not trivial, because this dense prediction task requ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Delving_Into_Shape-Aware_Zero-Shot_Semantic_Segmentation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Delving_Into_Shape-Aware_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.08491 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Delving_Into_Shape-Aware_Zero-Shot_Semantic_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Delving_Into_Shape-Aware_Zero-Shot_Semantic_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Post-Training Quantization on Diffusion Models | Yuzhang Shang, Zhihang Yuan, Bin Xie, Bingzhe Wu, Yan Yan | Denoising diffusion (score-based) generative models have recently achieved significant accomplishments in generating realistic and diverse data. These approaches define a forward diffusion process for transforming data into noise and a backward denoising process for sampling data from noise. Unfortunately, the generati... | https://openaccess.thecvf.com/content/CVPR2023/papers/Shang_Post-Training_Quantization_on_Diffusion_Models_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Shang_Post-Training_Quantization_on_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.15736 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Shang_Post-Training_Quantization_on_Diffusion_Models_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Shang_Post-Training_Quantization_on_Diffusion_Models_CVPR_2023_paper.html | CVPR 2023 | null |
Adaptive Global Decay Process for Event Cameras | Urbano Miguel Nunes, Ryad Benosman, Sio-Hoi Ieng | In virtually all event-based vision problems, there is the need to select the most recent events, which are assumed to carry the most relevant information content. To achieve this, at least one of three main strategies is applied, namely: 1) constant temporal decay or fixed time window, 2) constant number of events, an... | https://openaccess.thecvf.com/content/CVPR2023/papers/Nunes_Adaptive_Global_Decay_Process_for_Event_Cameras_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Nunes_Adaptive_Global_Decay_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Nunes_Adaptive_Global_Decay_Process_for_Event_Cameras_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Nunes_Adaptive_Global_Decay_Process_for_Event_Cameras_CVPR_2023_paper.html | CVPR 2023 | null |
Multi-Space Neural Radiance Fields | Ze-Xin Yin, Jiaxiong Qiu, Ming-Ming Cheng, Bo Ren | Neural Radiance Fields (NeRF) and its variants have reached state-of-the-art performance in many novel-view-synthesis-related tasks. However, current NeRF-based methods still suffer from the existence of reflective objects, often resulting in blurry or distorted rendering. Instead of calculating a single radiance field... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yin_Multi-Space_Neural_Radiance_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yin_Multi-Space_Neural_Radiance_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.04268 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_Multi-Space_Neural_Radiance_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yin_Multi-Space_Neural_Radiance_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
Leveraging Inter-Rater Agreement for Classification in the Presence of Noisy Labels | Maria Sofia Bucarelli, Lucas Cassano, Federico Siciliano, Amin Mantrach, Fabrizio Silvestri | In practical settings, classification datasets are obtained through a labelling process that is usually done by humans. Labels can be noisy as they are obtained by aggregating the different individual labels assigned to the same sample by multiple, and possibly disagreeing, annotators. The inter-rater agreement on thes... | https://openaccess.thecvf.com/content/CVPR2023/papers/Bucarelli_Leveraging_Inter-Rater_Agreement_for_Classification_in_the_Presence_of_Noisy_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Bucarelli_Leveraging_Inter-Rater_Agreement_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Bucarelli_Leveraging_Inter-Rater_Agreement_for_Classification_in_the_Presence_of_Noisy_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Bucarelli_Leveraging_Inter-Rater_Agreement_for_Classification_in_the_Presence_of_Noisy_CVPR_2023_paper.html | CVPR 2023 | null |
Bitstream-Corrupted JPEG Images Are Restorable: Two-Stage Compensation and Alignment Framework for Image Restoration | Wenyang Liu, Yi Wang, Kim-Hui Yap, Lap-Pui Chau | In this paper, we study a real-world JPEG image restoration problem with bit errors on the encrypted bitstream. The bit errors bring unpredictable color casts and block shifts on decoded image contents, which cannot be trivially resolved by existing image restoration methods mainly relying on pre-defined degradation mo... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liu_Bitstream-Corrupted_JPEG_Images_Are_Restorable_Two-Stage_Compensation_and_Alignment_Framework_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Liu_Bitstream-Corrupted_JPEG_Images_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.06976 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Bitstream-Corrupted_JPEG_Images_Are_Restorable_Two-Stage_Compensation_and_Alignment_Framework_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liu_Bitstream-Corrupted_JPEG_Images_Are_Restorable_Two-Stage_Compensation_and_Alignment_Framework_CVPR_2023_paper.html | CVPR 2023 | null |
Analyzing Physical Impacts Using Transient Surface Wave Imaging | Tianyuan Zhang, Mark Sheinin, Dorian Chan, Mark Rau, Matthew O’Toole, Srinivasa G. Narasimhan | The subtle vibrations on an object's surface contain information about the object's physical properties and its interaction with the environment. Prior works imaged surface vibration to recover the object's material properties via modal analysis, which discards the transient vibrations propagating immediately after the... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_Analyzing_Physical_Impacts_Using_Transient_Surface_Wave_Imaging_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_Analyzing_Physical_Impacts_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Analyzing_Physical_Impacts_Using_Transient_Surface_Wave_Imaging_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_Analyzing_Physical_Impacts_Using_Transient_Surface_Wave_Imaging_CVPR_2023_paper.html | CVPR 2023 | null |
X-Pruner: eXplainable Pruning for Vision Transformers | Lu Yu, Wei Xiang | Recently vision transformer models have become prominent models for a range of tasks. These models, however, usually suffer from intensive computational costs and heavy memory requirements, making them impractical for deployment on edge platforms. Recent studies have proposed to prune transformers in an unexplainable m... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_X-Pruner_eXplainable_Pruning_for_Vision_Transformers_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_X-Pruner_eXplainable_Pruning_for_Vision_Transformers_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yu_X-Pruner_eXplainable_Pruning_for_Vision_Transformers_CVPR_2023_paper.html | CVPR 2023 | null |
Hard Sample Matters a Lot in Zero-Shot Quantization | Huantong Li, Xiangmiao Wu, Fanbing Lv, Daihai Liao, Thomas H. Li, Yonggang Zhang, Bo Han, Mingkui Tan | Zero-shot quantization (ZSQ) is promising for compressing and accelerating deep neural networks when the data for training full-precision models are inaccessible. In ZSQ, network quantization is performed using synthetic samples, thus, the performance of quantized models depends heavily on the quality of synthetic samp... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Hard_Sample_Matters_a_Lot_in_Zero-Shot_Quantization_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Hard_Sample_Matters_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.13826 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Hard_Sample_Matters_a_Lot_in_Zero-Shot_Quantization_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Hard_Sample_Matters_a_Lot_in_Zero-Shot_Quantization_CVPR_2023_paper.html | CVPR 2023 | null |
Meta Compositional Referring Expression Segmentation | Li Xu, Mark He Huang, Xindi Shang, Zehuan Yuan, Ying Sun, Jun Liu | Referring expression segmentation aims to segment an object described by a language expression from an image. Despite the recent progress on this task, existing models tackling this task may not be able to fully capture semantics and visual representations of individual concepts, which limits their generalization capab... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Meta_Compositional_Referring_Expression_Segmentation_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2304.04415 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Meta_Compositional_Referring_Expression_Segmentation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Meta_Compositional_Referring_Expression_Segmentation_CVPR_2023_paper.html | CVPR 2023 | null |
Histopathology Whole Slide Image Analysis With Heterogeneous Graph Representation Learning | Tsai Hor Chan, Fernando Julio Cendra, Lan Ma, Guosheng Yin, Lequan Yu | Graph-based methods have been extensively applied to whole slide histopathology image (WSI) analysis due to the advantage of modeling the spatial relationships among different entities. However, most of the existing methods focus on modeling WSIs with homogeneous graphs (e.g., with homogeneous node type). Despite their... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chan_Histopathology_Whole_Slide_Image_Analysis_With_Heterogeneous_Graph_Representation_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Chan_Histopathology_Whole_Slide_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chan_Histopathology_Whole_Slide_Image_Analysis_With_Heterogeneous_Graph_Representation_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chan_Histopathology_Whole_Slide_Image_Analysis_With_Heterogeneous_Graph_Representation_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
ScanDMM: A Deep Markov Model of Scanpath Prediction for 360deg Images | Xiangjie Sui, Yuming Fang, Hanwei Zhu, Shiqi Wang, Zhou Wang | Scanpath prediction for 360deg images aims to produce dynamic gaze behaviors based on the human visual perception mechanism. Most existing scanpath prediction methods for 360deg images do not give a complete treatment of the time-dependency when predicting human scanpath, resulting in inferior performance and poor gene... | https://openaccess.thecvf.com/content/CVPR2023/papers/Sui_ScanDMM_A_Deep_Markov_Model_of_Scanpath_Prediction_for_360deg_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sui_ScanDMM_A_Deep_Markov_Model_of_Scanpath_Prediction_for_360deg_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sui_ScanDMM_A_Deep_Markov_Model_of_Scanpath_Prediction_for_360deg_CVPR_2023_paper.html | CVPR 2023 | null |
Towards All-in-One Pre-Training via Maximizing Multi-Modal Mutual Information | Weijie Su, Xizhou Zhu, Chenxin Tao, Lewei Lu, Bin Li, Gao Huang, Yu Qiao, Xiaogang Wang, Jie Zhou, Jifeng Dai | To effectively exploit the potential of large-scale models, various pre-training strategies supported by massive data from different sources are proposed, including supervised pre-training, weakly-supervised pre-training, and self-supervised pre-training. It has been proved that combining multiple pre-training strategi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Su_Towards_All-in-One_Pre-Training_via_Maximizing_Multi-Modal_Mutual_Information_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Su_Towards_All-in-One_Pre-Training_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.09807 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Su_Towards_All-in-One_Pre-Training_via_Maximizing_Multi-Modal_Mutual_Information_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Su_Towards_All-in-One_Pre-Training_via_Maximizing_Multi-Modal_Mutual_Information_CVPR_2023_paper.html | CVPR 2023 | null |
Aligning Bag of Regions for Open-Vocabulary Object Detection | Size Wu, Wenwei Zhang, Sheng Jin, Wentao Liu, Chen Change Loy | Pre-trained vision-language models (VLMs) learn to align vision and language representations on large-scale datasets, where each image-text pair usually contains a bag of semantic concepts. However, existing open-vocabulary object detectors only align region embeddings individually with the corresponding features extra... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Aligning_Bag_of_Regions_for_Open-Vocabulary_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Aligning_Bag_of_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.13996 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Aligning_Bag_of_Regions_for_Open-Vocabulary_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Aligning_Bag_of_Regions_for_Open-Vocabulary_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Two-View Geometry Scoring Without Correspondences | Axel Barroso-Laguna, Eric Brachmann, Victor Adrian Prisacariu, Gabriel J. Brostow, Daniyar Turmukhambetov | Camera pose estimation for two-view geometry traditionally relies on RANSAC. Normally, a multitude of image correspondences leads to a pool of proposed hypotheses, which are then scored to find a winning model. The inlier count is generally regarded as a reliable indicator of "consensus". We examine this scoring heuris... | https://openaccess.thecvf.com/content/CVPR2023/papers/Barroso-Laguna_Two-View_Geometry_Scoring_Without_Correspondences_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Barroso-Laguna_Two-View_Geometry_Scoring_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Barroso-Laguna_Two-View_Geometry_Scoring_Without_Correspondences_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Barroso-Laguna_Two-View_Geometry_Scoring_Without_Correspondences_CVPR_2023_paper.html | CVPR 2023 | null |
Annealing-Based Label-Transfer Learning for Open World Object Detection | Yuqing Ma, Hainan Li, Zhange Zhang, Jinyang Guo, Shanghang Zhang, Ruihao Gong, Xianglong Liu | Open world object detection (OWOD) has attracted extensive attention due to its practicability in the real world. Previous OWOD works manually designed unknown-discover strategies to select unknown proposals from the background, suffering from uncertainties without appropriate priors. In this paper, we claim the learni... | https://openaccess.thecvf.com/content/CVPR2023/papers/Ma_Annealing-Based_Label-Transfer_Learning_for_Open_World_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ma_Annealing-Based_Label-Transfer_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Annealing-Based_Label-Transfer_Learning_for_Open_World_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Ma_Annealing-Based_Label-Transfer_Learning_for_Open_World_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Continual Semantic Segmentation With Automatic Memory Sample Selection | Lanyun Zhu, Tianrun Chen, Jianxiong Yin, Simon See, Jun Liu | Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training. To alleviate the catastrophic forgetting issue in CSS, a memory buffer that stores a small number of samples from the previous classes is constructed for replay. However, existing methods se... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhu_Continual_Semantic_Segmentation_With_Automatic_Memory_Sample_Selection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhu_Continual_Semantic_Segmentation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05015 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Continual_Semantic_Segmentation_With_Automatic_Memory_Sample_Selection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhu_Continual_Semantic_Segmentation_With_Automatic_Memory_Sample_Selection_CVPR_2023_paper.html | CVPR 2023 | null |
Meta-Tuning Loss Functions and Data Augmentation for Few-Shot Object Detection | Berkan Demirel, Orhun Buğra Baran, Ramazan Gokberk Cinbis | Few-shot object detection, the problem of modelling novel object detection categories with few training instances, is an emerging topic in the area of few-shot learning and object detection. Contemporary techniques can be divided into two groups: fine-tuning based and meta-learning based approaches. While meta-learning... | https://openaccess.thecvf.com/content/CVPR2023/papers/Demirel_Meta-Tuning_Loss_Functions_and_Data_Augmentation_for_Few-Shot_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Demirel_Meta-Tuning_Loss_Functions_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.12161 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Demirel_Meta-Tuning_Loss_Functions_and_Data_Augmentation_for_Few-Shot_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Demirel_Meta-Tuning_Loss_Functions_and_Data_Augmentation_for_Few-Shot_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
A Light Weight Model for Active Speaker Detection | Junhua Liao, Haihan Duan, Kanghui Feng, Wanbing Zhao, Yanbing Yang, Liangyin Chen | Active speaker detection is a challenging task in audio-visual scenarios, with the aim to detect who is speaking in one or more speaker scenarios. This task has received considerable attention because it is crucial in many applications. Existing studies have attempted to improve the performance by inputting multiple ca... | https://openaccess.thecvf.com/content/CVPR2023/papers/Liao_A_Light_Weight_Model_for_Active_Speaker_Detection_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.04439 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_A_Light_Weight_Model_for_Active_Speaker_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Liao_A_Light_Weight_Model_for_Active_Speaker_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
Self-Supervised Video Forensics by Audio-Visual Anomaly Detection | Chao Feng, Ziyang Chen, Andrew Owens | Manipulated videos often contain subtle inconsistencies between their visual and audio signals. We propose a video forensics method, based on anomaly detection, that can identify these inconsistencies, and that can be trained solely using real, unlabeled data. We train an autoregressive model to generate sequences of a... | https://openaccess.thecvf.com/content/CVPR2023/papers/Feng_Self-Supervised_Video_Forensics_by_Audio-Visual_Anomaly_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Feng_Self-Supervised_Video_Forensics_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2301.01767 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Self-Supervised_Video_Forensics_by_Audio-Visual_Anomaly_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Feng_Self-Supervised_Video_Forensics_by_Audio-Visual_Anomaly_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
CLIP2Scene: Towards Label-Efficient 3D Scene Understanding by CLIP | Runnan Chen, Youquan Liu, Lingdong Kong, Xinge Zhu, Yuexin Ma, Yikang Li, Yuenan Hou, Yu Qiao, Wenping Wang | Contrastive Language-Image Pre-training (CLIP) achieves promising results in 2D zero-shot and few-shot learning. Despite the impressive performance in 2D, applying CLIP to help the learning in 3D scene understanding has yet to be explored. In this paper, we make the first attempt to investigate how CLIP knowledge benef... | https://openaccess.thecvf.com/content/CVPR2023/papers/Chen_CLIP2Scene_Towards_Label-Efficient_3D_Scene_Understanding_by_CLIP_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2301.04926 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_CLIP2Scene_Towards_Label-Efficient_3D_Scene_Understanding_by_CLIP_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Chen_CLIP2Scene_Towards_Label-Efficient_3D_Scene_Understanding_by_CLIP_CVPR_2023_paper.html | CVPR 2023 | null |
GCFAgg: Global and Cross-View Feature Aggregation for Multi-View Clustering | Weiqing Yan, Yuanyang Zhang, Chenlei Lv, Chang Tang, Guanghui Yue, Liang Liao, Weisi Lin | Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent years. However, most existing deep clustering methods learn consensus representation or view-specific representations from multiple views v... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yan_GCFAgg_Global_and_Cross-View_Feature_Aggregation_for_Multi-View_Clustering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Yan_GCFAgg_Global_and_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2305.06799 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_GCFAgg_Global_and_Cross-View_Feature_Aggregation_for_Multi-View_Clustering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yan_GCFAgg_Global_and_Cross-View_Feature_Aggregation_for_Multi-View_Clustering_CVPR_2023_paper.html | CVPR 2023 | null |
Class Balanced Adaptive Pseudo Labeling for Federated Semi-Supervised Learning | Ming Li, Qingli Li, Yan Wang | This paper focuses on federated semi-supervised learning (FSSL), assuming that few clients have fully labeled data (labeled clients) and the training datasets in other clients are fully unlabeled (unlabeled clients). Existing methods attempt to deal with the challenges caused by not independent and identically distribu... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Class_Balanced_Adaptive_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Class_Balanced_Adaptive_Pseudo_Labeling_for_Federated_Semi-Supervised_Learning_CVPR_2023_paper.html | CVPR 2023 | null |
Rethinking Out-of-Distribution (OOD) Detection: Masked Image Modeling Is All You Need | Jingyao Li, Pengguang Chen, Zexin He, Shaozuo Yu, Shu Liu, Jiaya Jia | The core of out-of-distribution (OOD) detection is to learn the in-distribution (ID) representation, which is distinguishable from OOD samples. Previous work applied recognition-based methods to learn the ID features, which tend to learn shortcuts instead of comprehensive representations. In this work, we find surprisi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_Rethinking_Out-of-Distribution_OOD_Detection_Masked_Image_Modeling_Is_All_You_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_Rethinking_Out-of-Distribution_OOD_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2302.02615 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Rethinking_Out-of-Distribution_OOD_Detection_Masked_Image_Modeling_Is_All_You_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_Rethinking_Out-of-Distribution_OOD_Detection_Masked_Image_Modeling_Is_All_You_CVPR_2023_paper.html | CVPR 2023 | null |
DeGPR: Deep Guided Posterior Regularization for Multi-Class Cell Detection and Counting | Aayush Kumar Tyagi, Chirag Mohapatra, Prasenjit Das, Govind Makharia, Lalita Mehra, Prathosh AP, Mausam | Multi-class cell detection and counting is an essential task for many pathological diagnoses. Manual counting is tedious and often leads to inter-observer variations among pathologists. While there exist multiple, general-purpose, deep learning-based object detection and counting methods, they may not readily transfer ... | https://openaccess.thecvf.com/content/CVPR2023/papers/Tyagi_DeGPR_Deep_Guided_Posterior_Regularization_for_Multi-Class_Cell_Detection_and_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Tyagi_DeGPR_Deep_Guided_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.00741 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Tyagi_DeGPR_Deep_Guided_Posterior_Regularization_for_Multi-Class_Cell_Detection_and_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Tyagi_DeGPR_Deep_Guided_Posterior_Regularization_for_Multi-Class_Cell_Detection_and_CVPR_2023_paper.html | CVPR 2023 | null |
Masked Scene Contrast: A Scalable Framework for Unsupervised 3D Representation Learning | Xiaoyang Wu, Xin Wen, Xihui Liu, Hengshuang Zhao | As a pioneering work, PointContrast conducts unsupervised 3D representation learning via leveraging contrastive learning over raw RGB-D frames and proves its effectiveness on various downstream tasks. However, the trend of large-scale unsupervised learning in 3D has yet to emerge due to two stumbling blocks: the ineffi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Wu_Masked_Scene_Contrast_A_Scalable_Framework_for_Unsupervised_3D_Representation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Wu_Masked_Scene_Contrast_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2303.14191 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Masked_Scene_Contrast_A_Scalable_Framework_for_Unsupervised_3D_Representation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Wu_Masked_Scene_Contrast_A_Scalable_Framework_for_Unsupervised_3D_Representation_CVPR_2023_paper.html | CVPR 2023 | null |
Multi Domain Learning for Motion Magnification | Jasdeep Singh, Subrahmanyam Murala, G. Sankara Raju Kosuru | Video motion magnification makes subtle invisible motions visible, such as small chest movements while breathing, subtle vibrations in the moving objects etc. But small motions are prone to noise, illumination changes, large motions, etc. making the task difficult. Most state-of-the-art methods use hand-crafted concept... | https://openaccess.thecvf.com/content/CVPR2023/papers/Singh_Multi_Domain_Learning_for_Motion_Magnification_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Singh_Multi_Domain_Learning_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_Multi_Domain_Learning_for_Motion_Magnification_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Singh_Multi_Domain_Learning_for_Motion_Magnification_CVPR_2023_paper.html | CVPR 2023 | null |
LOGO: A Long-Form Video Dataset for Group Action Quality Assessment | Shiyi Zhang, Wenxun Dai, Sujia Wang, Xiangwei Shen, Jiwen Lu, Jie Zhou, Yansong Tang | Action quality assessment (AQA) has become an emerging topic since it can be extensively applied in numerous scenarios. However, most existing methods and datasets focus on single-person short-sequence scenes, hindering the application of AQA in more complex situations. To address this issue, we construct a new multi-p... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_LOGO_A_Long-Form_Video_Dataset_for_Group_Action_Quality_Assessment_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_LOGO_A_Long-Form_CVPR_2023_supplemental.zip | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_LOGO_A_Long-Form_Video_Dataset_for_Group_Action_Quality_Assessment_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_LOGO_A_Long-Form_Video_Dataset_for_Group_Action_Quality_Assessment_CVPR_2023_paper.html | CVPR 2023 | null |
A Simple Baseline for Video Restoration With Grouped Spatial-Temporal Shift | Dasong Li, Xiaoyu Shi, Yi Zhang, Ka Chun Cheung, Simon See, Xiaogang Wang, Hongwei Qin, Hongsheng Li | Video restoration, which aims to restore clear frames from degraded videos, has numerous important applications. The key to video restoration depends on utilizing inter-frame information. However, existing deep learning methods often rely on complicated network architectures, such as optical flow estimation, deformable... | https://openaccess.thecvf.com/content/CVPR2023/papers/Li_A_Simple_Baseline_for_Video_Restoration_With_Grouped_Spatial-Temporal_Shift_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Li_A_Simple_Baseline_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Li_A_Simple_Baseline_for_Video_Restoration_With_Grouped_Spatial-Temporal_Shift_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Li_A_Simple_Baseline_for_Video_Restoration_With_Grouped_Spatial-Temporal_Shift_CVPR_2023_paper.html | CVPR 2023 | null |
UniSim: A Neural Closed-Loop Sensor Simulator | Ze Yang, Yun Chen, Jingkang Wang, Sivabalan Manivasagam, Wei-Chiu Ma, Anqi Joyce Yang, Raquel Urtasun | Rigorously testing autonomy systems is essential for making safe self-driving vehicles (SDV) a reality. It requires one to generate safety critical scenarios beyond what can be collected safely in the world, as many scenarios happen rarely on our roads. To accurately evaluate performance, we need to test the SDV on the... | https://openaccess.thecvf.com/content/CVPR2023/papers/Yang_UniSim_A_Neural_Closed-Loop_Sensor_Simulator_CVPR_2023_paper.pdf | null | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_UniSim_A_Neural_Closed-Loop_Sensor_Simulator_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Yang_UniSim_A_Neural_Closed-Loop_Sensor_Simulator_CVPR_2023_paper.html | CVPR 2023 | null |
itKD: Interchange Transfer-Based Knowledge Distillation for 3D Object Detection | Hyeon Cho, Junyong Choi, Geonwoo Baek, Wonjun Hwang | Point-cloud based 3D object detectors recently have achieved remarkable progress. However, most studies are limited to the development of network architectures for improving only their accuracy without consideration of the computational efficiency. In this paper, we first propose an autoencoder-style framework comprisi... | https://openaccess.thecvf.com/content/CVPR2023/papers/Cho_itKD_Interchange_Transfer-Based_Knowledge_Distillation_for_3D_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Cho_itKD_Interchange_Transfer-Based_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2205.15531 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_itKD_Interchange_Transfer-Based_Knowledge_Distillation_for_3D_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Cho_itKD_Interchange_Transfer-Based_Knowledge_Distillation_for_3D_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
SliceMatch: Geometry-Guided Aggregation for Cross-View Pose Estimation | Ted Lentsch, Zimin Xia, Holger Caesar, Julian F. P. Kooij | This work addresses cross-view camera pose estimation, i.e., determining the 3-Degrees-of-Freedom camera pose of a given ground-level image w.r.t. an aerial image of the local area. We propose SliceMatch, which consists of ground and aerial feature extractors, feature aggregators, and a pose predictor. The feature extr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lentsch_SliceMatch_Geometry-Guided_Aggregation_for_Cross-View_Pose_Estimation_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lentsch_SliceMatch_Geometry-Guided_Aggregation_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2211.14651 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lentsch_SliceMatch_Geometry-Guided_Aggregation_for_Cross-View_Pose_Estimation_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lentsch_SliceMatch_Geometry-Guided_Aggregation_for_Cross-View_Pose_Estimation_CVPR_2023_paper.html | CVPR 2023 | null |
2PCNet: Two-Phase Consistency Training for Day-to-Night Unsupervised Domain Adaptive Object Detection | Mikhail Kennerley, Jian-Gang Wang, Bharadwaj Veeravalli, Robby T. Tan | Object detection at night is a challenging problem due to the absence of night image annotations. Despite several domain adaptation methods, achieving high-precision results remains an issue. False-positive error propagation is still observed in methods using the well-established student-teacher framework, particularly... | https://openaccess.thecvf.com/content/CVPR2023/papers/Kennerley_2PCNet_Two-Phase_Consistency_Training_for_Day-to-Night_Unsupervised_Domain_Adaptive_Object_CVPR_2023_paper.pdf | null | http://arxiv.org/abs/2303.13853 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Kennerley_2PCNet_Two-Phase_Consistency_Training_for_Day-to-Night_Unsupervised_Domain_Adaptive_Object_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Kennerley_2PCNet_Two-Phase_Consistency_Training_for_Day-to-Night_Unsupervised_Domain_Adaptive_Object_CVPR_2023_paper.html | CVPR 2023 | null |
Prefix Conditioning Unifies Language and Label Supervision | Kuniaki Saito, Kihyuk Sohn, Xiang Zhang, Chun-Liang Li, Chen-Yu Lee, Kate Saenko, Tomas Pfister | Pretraining visual models on web-scale image-caption datasets has recently emerged as a powerful alternative to traditional pretraining on image classification data. Image-caption datasets are more "open-domain", containing broader scene types and vocabulary words, and result in models that have strong performance in f... | https://openaccess.thecvf.com/content/CVPR2023/papers/Saito_Prefix_Conditioning_Unifies_Language_and_Label_Supervision_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Saito_Prefix_Conditioning_Unifies_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2206.01125 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Saito_Prefix_Conditioning_Unifies_Language_and_Label_Supervision_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Saito_Prefix_Conditioning_Unifies_Language_and_Label_Supervision_CVPR_2023_paper.html | CVPR 2023 | null |
Panoptic Lifting for 3D Scene Understanding With Neural Fields | Yawar Siddiqui, Lorenzo Porzi, Samuel Rota Bulò, Norman Müller, Matthias Nießner, Angela Dai, Peter Kontschieder | We propose Panoptic Lifting, a novel approach for learning panoptic 3D volumetric representations from images of in-the-wild scenes. Once trained, our model can render color images together with 3D-consistent panoptic segmentation from novel viewpoints. Unlike existing approaches which use 3D input directly or indirect... | https://openaccess.thecvf.com/content/CVPR2023/papers/Siddiqui_Panoptic_Lifting_for_3D_Scene_Understanding_With_Neural_Fields_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Siddiqui_Panoptic_Lifting_for_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Siddiqui_Panoptic_Lifting_for_3D_Scene_Understanding_With_Neural_Fields_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Siddiqui_Panoptic_Lifting_for_3D_Scene_Understanding_With_Neural_Fields_CVPR_2023_paper.html | CVPR 2023 | null |
WeatherStream: Light Transport Automation of Single Image Deweathering | Howard Zhang, Yunhao Ba, Ethan Yang, Varan Mehra, Blake Gella, Akira Suzuki, Arnold Pfahnl, Chethan Chinder Chandrappa, Alex Wong, Achuta Kadambi | Today single image deweathering is arguably more sensitive to the dataset type, rather than the model. We introduce WeatherStream, an automatic pipeline capturing all real-world weather effects (rain, snow, and rain fog degradations), along with their clean image pairs. Previous state-of-the-art methods that have attem... | https://openaccess.thecvf.com/content/CVPR2023/papers/Zhang_WeatherStream_Light_Transport_Automation_of_Single_Image_Deweathering_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Zhang_WeatherStream_Light_Transport_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_WeatherStream_Light_Transport_Automation_of_Single_Image_Deweathering_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Zhang_WeatherStream_Light_Transport_Automation_of_Single_Image_Deweathering_CVPR_2023_paper.html | CVPR 2023 | null |
Learning To Detect Mirrors From Videos via Dual Correspondences | Jiaying Lin, Xin Tan, Rynson W.H. Lau | Detecting mirrors from static images has received significant research interest recently. However, detecting mirrors over dynamic scenes is still under-explored due to the lack of a high-quality dataset and an effective method for video mirror detection (VMD). To the best of our knowledge, this is the first work to add... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lin_Learning_To_Detect_Mirrors_From_Videos_via_Dual_Correspondences_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lin_Learning_To_Detect_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Learning_To_Detect_Mirrors_From_Videos_via_Dual_Correspondences_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lin_Learning_To_Detect_Mirrors_From_Videos_via_Dual_Correspondences_CVPR_2023_paper.html | CVPR 2023 | null |
Single View Scene Scale Estimation Using Scale Field | Byeong-Uk Lee, Jianming Zhang, Yannick Hold-Geoffroy, In So Kweon | In this paper, we propose a single image scale estimation method based on a novel scale field representation. A scale field defines the local pixel-to-metric conversion ratio along the gravity direction on all the ground pixels. This representation resolves the ambiguity in camera parameters, allowing us to use a simpl... | https://openaccess.thecvf.com/content/CVPR2023/papers/Lee_Single_View_Scene_Scale_Estimation_Using_Scale_Field_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Lee_Single_View_Scene_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Single_View_Scene_Scale_Estimation_Using_Scale_Field_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Lee_Single_View_Scene_Scale_Estimation_Using_Scale_Field_CVPR_2023_paper.html | CVPR 2023 | null |
Learning Semantic-Aware Disentangled Representation for Flexible 3D Human Body Editing | Xiaokun Sun, Qiao Feng, Xiongzheng Li, Jinsong Zhang, Yu-Kun Lai, Jingyu Yang, Kun Li | 3D human body representation learning has received increasing attention in recent years. However, existing works cannot flexibly, controllably and accurately represent human bodies, limited by coarse semantics and unsatisfactory representation capability, particularly in the absence of supervised data. In this paper, w... | https://openaccess.thecvf.com/content/CVPR2023/papers/Sun_Learning_Semantic-Aware_Disentangled_Representation_for_Flexible_3D_Human_Body_Editing_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Sun_Learning_Semantic-Aware_Disentangled_CVPR_2023_supplemental.pdf | null | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Learning_Semantic-Aware_Disentangled_Representation_for_Flexible_3D_Human_Body_Editing_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Sun_Learning_Semantic-Aware_Disentangled_Representation_for_Flexible_3D_Human_Body_Editing_CVPR_2023_paper.html | CVPR 2023 | null |
Generating Features With Increased Crop-Related Diversity for Few-Shot Object Detection | Jingyi Xu, Hieu Le, Dimitris Samaras | Two-stage object detectors generate object proposals and classify them to detect objects in images. These proposals often do not perfectly contain the objects but overlap with them in many possible ways, exhibiting great variability in the difficulty levels of the proposals. Training a robust classifier against this cr... | https://openaccess.thecvf.com/content/CVPR2023/papers/Xu_Generating_Features_With_Increased_Crop-Related_Diversity_for_Few-Shot_Object_Detection_CVPR_2023_paper.pdf | https://openaccess.thecvf.com/content/CVPR2023/supplemental/Xu_Generating_Features_With_CVPR_2023_supplemental.pdf | http://arxiv.org/abs/2304.05096 | https://openaccess.thecvf.com | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Generating_Features_With_Increased_Crop-Related_Diversity_for_Few-Shot_Object_Detection_CVPR_2023_paper.html | https://openaccess.thecvf.com/content/CVPR2023/html/Xu_Generating_Features_With_Increased_Crop-Related_Diversity_for_Few-Shot_Object_Detection_CVPR_2023_paper.html | CVPR 2023 | null |
End of preview. Expand in Data Studio
CVPR 2023 Accepted Paper Meta Info Dataset
This dataset is collect from the CVPR 2023 Open Access website (https://openaccess.thecvf.com/CVPR2023) as well as the arxiv website DeepNLP paper arxiv (http://www.deepnlp.org/content/paper/cvpr2023). For researchers who are interested in doing analysis of CVPR 2023 accepted papers and potential trends, you can use the already cleaned up json files. Each row contains the meta information of a paper in the CVPR 2024 conference. To explore more AI & Robotic papers (NIPS/ICML/ICLR/IROS/ICRA/etc) and AI equations, feel free to navigate the Equation Search Engine (http://www.deepnlp.org/search/equation) as well as the AI Agent Search Engine to find the deployed AI Apps and Agents (http://www.deepnlp.org/search/agent) in your domain.
Equations Latex code and Papers Search Engine
Meta Information of Json File of Paper
{
"title": "GFPose: Learning 3D Human Pose Prior With Gradient Fields",
"authors": "Hai Ci, Mingdong Wu, Wentao Zhu, Xiaoxuan Ma, Hao Dong, Fangwei Zhong, Yizhou Wang",
"abstract": "Learning 3D human pose prior is essential to human-centered AI. Here, we present GFPose, a versatile framework to model plausible 3D human poses for various applications. At the core of GFPose is a time-dependent score network, which estimates the gradient on each body joint and progressively denoises the perturbed 3D human pose to match a given task specification. During the denoising process, GFPose implicitly incorporates pose priors in gradients and unifies various discriminative and generative tasks in an elegant framework. Despite the simplicity, GFPose demonstrates great potential in several downstream tasks. Our experiments empirically show that 1) as a multi-hypothesis pose estimator, GFPose outperforms existing SOTAs by 20% on Human3.6M dataset. 2) as a single-hypothesis pose estimator, GFPose achieves comparable results to deterministic SOTAs, even with a vanilla backbone. 3) GFPose is able to produce diverse and realistic samples in pose denoising, completion and generation tasks.",
"pdf": "https://openaccess.thecvf.com/content/CVPR2023/papers/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.pdf",
"supp": "https://openaccess.thecvf.com/content/CVPR2023/supplemental/Ci_GFPose_Learning_3D_CVPR_2023_supplemental.pdf",
"arXiv": "http://arxiv.org/abs/2212.08641",
"bibtex": "https://openaccess.thecvf.com",
"url": "https://openaccess.thecvf.com/content/CVPR2023/html/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.html",
"detail_url": "https://openaccess.thecvf.com/content/CVPR2023/html/Ci_GFPose_Learning_3D_Human_Pose_Prior_With_Gradient_Fields_CVPR_2023_paper.html",
"tags": "CVPR 2023"
}
Related
AI Agent Marketplace and Search
AI Agent Marketplace and Search
Robot Search
Equation and Academic search
AI & Robot Comprehensive Search
AI & Robot Question
AI & Robot Community
AI Agent Marketplace Blog
AI Agent Reviews
AI Agent Marketplace Directory
Microsoft AI Agents Reviews
Claude AI Agents Reviews
OpenAI AI Agents Reviews
Saleforce AI Agents Reviews
AI Agent Builder Reviews
AI Equation
List of AI Equations and Latex
List of Math Equations and Latex
List of Physics Equations and Latex
List of Statistics Equations and Latex
List of Machine Learning Equations and Latex
- Downloads last month
- 7
Size of downloaded dataset files:
5.07 MB
Size of the auto-converted Parquet files:
2.32 MB
Number of rows:
2,352