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检索条件"任意字段=2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023"
3320 条 记 录,以下是3271-3280 订阅
排序:
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly Supervised Semantic Segmentation
CLIP is Also an Efficient Segmenter: A Text-Driven Approach ...
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conference on computer vision and pattern recognition (CVPR)
作者: Yuqi Lin Minghao Chen Wenxiao Wang Boxi Wu Ke Li Binbin Lin Haifeng Liu Xiaofei He State Key Lab of CAD&CG College of Computer Science Zhejiang University School of Software Technology Zhejiang University Fullong Technology
Weakly supervised semantic segmentation (WSSS) with image-level labels is a challenging task. Mainstream approaches follow a multi-stage framework and suffer from high training costs. In this paper, we explore the pot...
来源: 评论
ACSeg: Adaptive Conceptualization for Unsupervised Semantic Segmentation
ACSeg: Adaptive Conceptualization for Unsupervised Semantic ...
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conference on computer vision and pattern recognition (CVPR)
作者: Kehan Li Zhennan Wang Zesen Cheng Runyi Yu Yian Zhao Guoli Song Chang Liu Li Yuan Jie Chen School of Electronic and Computer Engineering Peking University Shenzhen China AI for Science (AI4S)-Preferred Program Peking University Shenzhen Graduate School Shenzhen China Peng Cheng Laboratory Shenzhen China Dalian University of Technology Department of Automation and BNRist Tsinghua University Beijing China
Recently, self-supervised large-scale visual pre-training models have shown great promise in representing pixel-level semantic relationships, significantly promoting the development of unsupervised dense prediction ta...
来源: 评论
Minimizing the Accumulated Trajectory Error to Improve Dataset Distillation
Minimizing the Accumulated Trajectory Error to Improve Datas...
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conference on computer vision and pattern recognition (CVPR)
作者: Jiawei Du Yidi Jiang Vincent Y.F. Tan Joey Tianyi Zhou Haizhou Li Centre for Frontier AI Research (CFAR) Agency for Science Technology and Research (A*STAR) Singapore Agency for Science Technology and Research (A*STAR) Institute of High Performance Computing (IHPC) Singapore Department of Electrical and Computer Engineering National University of Singapore Department of Mathematics National University of Singapore SRIBD School of Data Science The Chinese University of Hong Kong Shenzhen China
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 com...
来源: 评论
Memory-Friendly Scalable Super-Resolution via Rewinding Lottery Ticket Hypothesis
Memory-Friendly Scalable Super-Resolution via Rewinding Lott...
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conference on computer vision and pattern recognition (CVPR)
作者: Jin Lin Xiaotong Luo Ming Hong Yanyun Qu Yuan Xie Zongze Wu School of Informatics Xiamen University Fujian China School of Computer Science and Technology East China Normal University Shanghai China School of Mechatronics and Control Engineering Shenzhen University Shenzhen China
Scalable deep Super-Resolution (SR) models are increasingly in demand, whose memory can be customized and tuned to the computational recourse of the platform. The existing dynamic scalable SR methods are not memory-fr...
来源: 评论
CAT: LoCalization and IdentificAtion Cascade Detection Transformer for Open-World Object Detection
CAT: LoCalization and IdentificAtion Cascade Detection Trans...
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conference on computer vision and pattern recognition (CVPR)
作者: Shuailei Ma Yuefeng Wang Ying Wei Jiaqi Fan Thomas H. Li Hongli Liu Fanbing Lv Northeast University Shenyang China Information Technology R&D Innovation Center of Peking University School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China Changsha Hisense Intelligent System Research Institute Co. Ltd
Open-world object detection (OWOD), as a more general and challenging goal, requires the model trained from data on known objects to detect both known and unknown objects and incrementally learn to identify these unkn...
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High-Frequency Stereo Matching Network
High-Frequency Stereo Matching Network
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conference on computer vision and pattern recognition (CVPR)
作者: Haoliang Zhao Huizhou Zhou Yongjun Zhang Jie Chen Yitong Yang Yong Zhao Text Computing & Cognitive Intelligence Engineering Research Center of National Education Ministry State Key Laboratory of Public Big Data College of Computer Science and Technology Institute of Artificial Intelligence Guizhou University Guiyang Guizhou China Ghost-Valley AI Technology Shenzhen Guangdong China School of Physics and Optoelectronic Engineering Guangdong University of Technology Guangzhou China The Key Laboratory of Integrated Microsystems Shenzhen Graduate School Peking University China
In the field of binocular stereo matching, remarkable progress has been made by iterative methods like RAFT-Stereo and CREStereo. However, most of these methods lose information during the iterative process, making it...
来源: 评论
Learning A Sparse Transformer Network for Effective Image Deraining
Learning A Sparse Transformer Network for Effective Image De...
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conference on computer vision and pattern recognition (CVPR)
作者: Xiang Chen Hao Li Mingqiang Li Jinshan Pan School of Computer Science and Engineering Nanjing University of Science and Technology Information Science Academy China Electronics Technology Group Corporation
Transformers-based methods have achieved significant performance in image deraining as they can model the non-local information which is vital for high-quality image reconstruction. In this paper, we find that most ex...
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CP3: Channel Pruning Plug-in for Point-Based Networks
CP3: Channel Pruning Plug-in for Point-Based Networks
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conference on computer vision and pattern recognition (CVPR)
作者: Yaomin Huang Ning Liu Zhengping Che Zhiyuan Xu Chaomin Shen Yaxin Peng Guixu Zhang Xinmei Liu Feifei Feng Jian Tang School of Computer Science East China Normal University Midea Group Department of Mathematics School of Science Shanghai University
Channel pruning can effectively reduce both computational cost and memory footprint of the original network while keeping a comparable accuracy performance. Though great success has been achieved in channel pruning fo...
来源: 评论
TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification
TranSG: Transformer-Based Skeleton Graph Prototype Contrasti...
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conference on computer vision and pattern recognition (CVPR)
作者: Haocong Rao Chunyan Miao LILY Research Center Nanyang Technological University Singapore School of Computer Science and Engineering Nanyang Technological University Singapore
Person re-identification (re-ID) via 3D skeleton data is an emerging topic with prominent advantages. Existing methods usually design skeleton descriptors with raw body joints or perform skeleton sequence representati...
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SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal Estimation of Point Clouds
SHS-Net: Learning Signed Hyper Surfaces for Oriented Normal ...
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conference on computer vision and pattern recognition (CVPR)
作者: Qing Li Huifang Feng Kanle Shi Yue Gao Yi Fang Yu-Shen Liu Zhizhong Han BNRist School of Software Tsinghua University Beijing China School of Informatics Xiamen University Xiamen China Kuaishou Technology Beijing China Center for Artificial Intelligence and Robotics New York University Abu Dhabi Abu Dhabi UAE Department of Computer Science Wayne State University Detroit USA
We propose a novel method called SHS-Net for oriented normal estimation of point clouds by learning signed hyper surfaces, which can accurately predict normals with global consistent orientation from various point clo...
来源: 评论