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检索条件"任意字段=IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000"
19489 条 记 录,以下是4651-4660 订阅
排序:
Physics-based Iterative Projection Complex Neural Network for Phase Retrieval in Lensless Microscopy Imaging
Physics-based Iterative Projection Complex Neural Network fo...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Zhang, Feilong Liu, Xianming Guo, Cheng Lin, Shiyi Jiang, Junjun Ji, Xiangyang Harbin Inst Technol Harbin Peoples R China Tsinghua Univ Beijing Peoples R China
Phase retrieval from intensity-only measurements plays a central role in many real-world imaging tasks. In recent years, deep neural networks based methods emerge and show promising performance for phase retrieval. Ho... 详细信息
来源: 评论
Neural Body: Implicit Neural Representations with Structured Latent Codes for Novel View Synthesis of Dynamic Humans
Neural Body: Implicit Neural Representations with Structured...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Peng, Sida Zhang, Yuanqing Xu, Yinghao Wang, Qianqian Shuai, Qing Bao, Hujun Zhou, Xiaowei Zhejiang Univ Hangzhou Peoples R China Chinese Univ Hong Kong Hong Kong Peoples R China Cornell Univ Ithaca NY 14853 USA
This paper addresses the challenge of novel view synthesis for a human performer from a very sparse set of camera views. Some recent works have shown that learning implicit neural representations of 3D scenes achieves... 详细信息
来源: 评论
Coarse-to-Fine Person Re-Identification with Auxiliary-Domain Classification and Second-Order Information Bottleneck
Coarse-to-Fine Person Re-Identification with Auxiliary-Domai...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Zhang, Anguo Gao, Yueming Niu, Yuzhen Liu, Wenxi Zhou, Yongcheng Fuzhou Univ Coll Phys & Informat Engn Fuzhou Peoples R China Key Lab Med Instrumentat & Pharmaceut Technol Fuj Fuzhou Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China
Person re-identification (Re-ID) is to retrieve a particular person captured by different cameras, which is of great significance for security surveillance and pedestrian behavior analysis. However, due to the large i... 详细信息
来源: 评论
Adaptive Methods for Real-World Domain Generalization
Adaptive Methods for Real-World Domain Generalization
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Dubey, Abhimanyu Ramanathan, Vignesh Pentland, Alex Mahajan, Dhruv MIT Cambridge MA 02139 USA Facebook AI Cambridge MA USA
Invariant approaches have been remarkably successful in tackling the problem of domain generalization, where the objective is to perform inference on data distributions different from those used in training. In our wo... 详细信息
来源: 评论
Mutual Graph Learning for Camouflaged Object Detection
Mutual Graph Learning for Camouflaged Object Detection
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Zhai, Qiang Li, Xin Yang, Fan Chen, Chenglizhao Cheng, Hong Fan, Deng-Ping UESTC Chengdu Peoples R China Grp 42 G42 Abu Dhabi U Arab Emirates Qingdao Univ Qingdao Peoples R China Incept Inst AI IIAI Abu Dhabi U Arab Emirates
Automatically detecting/segmenting object(s) that blend in with their surroundings is difficult for current models. A major challenge is that the intrinsic similarities between such foreground objects and background s... 详细信息
来源: 评论
Generating Non-Stationary Textures Using Self-Rectification
Generating Non-Stationary Textures Using Self-Rectification
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conference on computer vision and pattern recognition (cvpr)
作者: Yang Zhou Rongjun Xiao Dani Lischinski Daniel Cohen-Or Hui Huang Shenzhen University The Hebrew University of Jerusalem Tel Aviv University
This paper addresses the challenge of example-based non-stationary texture synthesis. We introduce a novel two-step approach wherein users first modify a reference texture using standard image editing tools, yielding ... 详细信息
来源: 评论
Self-Guided and Cross-Guided Learning for Few-Shot Segmentation
Self-Guided and Cross-Guided Learning for Few-Shot Segmentat...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Zhang, Bingfeng Xiao, Jimin Qin, Terry XJTLU Suzhou Peoples R China Univ Liverpool Liverpool Merseyside England Dinnar Automat Technol Suzhou Peoples R China
Few-shot segmentation has been attracting a lot of attention due to its effectiveness to segment unseen object classes with a few annotated samples. Most existing approaches use masked Global Average Pooling (GAP) to ... 详细信息
来源: 评论
Closing the Loop: Joint Rain Generation and Removal via Disentangled Image Translation
Closing the Loop: Joint Rain Generation and Removal via Dise...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Ye, Yuntong Chang, Yi Zhou, Hanyu Yan, Luxin Huazhong Univ Sci & Technol Sch Artificial Intelligence & Automat Natl Key Lab Sci & Technol Multispectral Informat Wuhan Peoples R China Peng Cheng Lab AI Ctr Shenzhen Peoples R China
Existing deep learning-based image deraining methods have achieved promising performance for synthetic rainy images, typically rely on the pairs of sharp images and simulated rainy counterparts. However, these methods... 详细信息
来源: 评论
POPDG: Popular 3D Dance Generation with PopDanceSet
POPDG: Popular 3D Dance Generation with PopDanceSet
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conference on computer vision and pattern recognition (cvpr)
作者: Zhenye Luo Min Ren Xuecai Hu Yongzhen Huang Li Yao School of Artificial Intelligencem Beijing Normal University
Generating dances that are both lifelike and well-aligned with music continues to be a challenging task in the cross-modal domain. This paper introduces PopDanceSet, the first dataset tailored to the preferences of yo... 详细信息
来源: 评论
Wide-Baseline Multi-Camera Calibration using Person Re-Identification
Wide-Baseline Multi-Camera Calibration using Person Re-Ident...
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ieee/CVF conference on computer vision and pattern recognition (cvpr)
作者: Xu, Yan Li, Yu-Jhe Weng, Xinshuo Kitani, Kris Carnegie Mellon Univ Pittsburgh PA 15213 USA
We address the problem of estimating the 3D pose of a network of cameras for large-environment wide-baseline scenarios, e.g., cameras for construction sites, sports stadiums, and public spaces. This task is challengin... 详细信息
来源: 评论