咨询与建议

限定检索结果

文献类型

  • 12,844 篇 会议
  • 13 篇 期刊文献
  • 2 册 图书

馆藏范围

  • 12,859 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 7,573 篇 工学
    • 6,863 篇 计算机科学与技术...
    • 880 篇 机械工程
    • 814 篇 软件工程
    • 435 篇 控制科学与工程
    • 360 篇 光学工程
    • 306 篇 电气工程
    • 209 篇 仪器科学与技术
    • 124 篇 信息与通信工程
    • 91 篇 生物工程
    • 62 篇 生物医学工程(可授...
    • 39 篇 电子科学与技术(可...
    • 34 篇 安全科学与工程
    • 26 篇 化学工程与技术
    • 21 篇 交通运输工程
    • 20 篇 建筑学
    • 18 篇 土木工程
  • 2,957 篇 医学
    • 2,956 篇 临床医学
    • 15 篇 基础医学(可授医学...
    • 12 篇 药学(可授医学、理...
  • 700 篇 理学
    • 359 篇 物理学
    • 225 篇 数学
    • 175 篇 系统科学
    • 95 篇 统计学(可授理学、...
    • 93 篇 生物学
    • 22 篇 化学
  • 201 篇 艺术学
    • 201 篇 设计学(可授艺术学...
  • 84 篇 管理学
    • 59 篇 图书情报与档案管...
    • 25 篇 管理科学与工程(可...
    • 14 篇 工商管理
  • 23 篇 法学
    • 21 篇 社会学
  • 5 篇 农学
  • 4 篇 教育学
  • 2 篇 经济学
  • 1 篇 军事学

主题

  • 6,464 篇 computer vision
  • 2,688 篇 training
  • 2,437 篇 pattern recognit...
  • 1,780 篇 computational mo...
  • 1,522 篇 visualization
  • 1,348 篇 three-dimensiona...
  • 1,091 篇 computer archite...
  • 1,063 篇 semantics
  • 997 篇 benchmark testin...
  • 976 篇 codes
  • 970 篇 conferences
  • 854 篇 feature extracti...
  • 830 篇 cameras
  • 771 篇 task analysis
  • 707 篇 deep learning
  • 646 篇 image segmentati...
  • 611 篇 object detection
  • 595 篇 shape
  • 554 篇 transformers
  • 538 篇 neural networks

机构

  • 132 篇 univ sci & techn...
  • 122 篇 carnegie mellon ...
  • 120 篇 tsinghua univ pe...
  • 114 篇 univ chinese aca...
  • 113 篇 chinese univ hon...
  • 94 篇 tsinghua univers...
  • 91 篇 zhejiang univ pe...
  • 91 篇 swiss fed inst t...
  • 85 篇 peng cheng lab p...
  • 81 篇 university of ch...
  • 80 篇 zhejiang univers...
  • 77 篇 shanghai ai lab ...
  • 77 篇 peng cheng labor...
  • 75 篇 university of sc...
  • 69 篇 shanghai jiao to...
  • 68 篇 shanghai jiao to...
  • 67 篇 alibaba grp peop...
  • 67 篇 stanford univ st...
  • 66 篇 univ hong kong p...
  • 64 篇 sensetime res pe...

作者

  • 77 篇 timofte radu
  • 63 篇 van gool luc
  • 45 篇 zhang lei
  • 36 篇 yang yi
  • 36 篇 luc van gool
  • 34 篇 tao dacheng
  • 31 篇 loy chen change
  • 29 篇 chen chen
  • 28 篇 sun jian
  • 28 篇 qi tian
  • 25 篇 li xin
  • 24 篇 liu yang
  • 24 篇 tian qi
  • 24 篇 ying shan
  • 23 篇 wang xinchao
  • 23 篇 zha zheng-jun
  • 23 篇 boxin shi
  • 21 篇 zhou jie
  • 21 篇 vasconcelos nuno
  • 20 篇 luo ping

语言

  • 12,851 篇 英文
  • 7 篇 其他
  • 1 篇 中文
检索条件"任意字段=IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops"
12859 条 记 录,以下是251-260 订阅
排序:
Dual-Branch Collaborative Transformer for Virtual Try-On
Dual-Branch Collaborative Transformer for Virtual Try-On
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Fenocchi, Emanuele Morelli, Davide Cornia, Marcella Baraldi, Lorenzo Cesari, Fabio Cucchiara, Rita Univ Modena & Reggio Emilia Modena Italy YOOX NET PORTER GRP Milan Italy
Image-based virtual try-on has recently gained a lot of attention in both the scientific and fashion industry communities due to its challenging setting and practical real-world applications. While pure convolutional ... 详细信息
来源: 评论
Deep Learning based Spatial-Temporal In-loop filtering for Versatile Video Coding
Deep Learning based Spatial-Temporal In-loop filtering for V...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Pham, Chi D. K. Fu, Chen Zhou, Jinjia Hosei Univ Tokyo Japan JST PRESTO Saitama Japan
The existing deep learning-based Versatile Video Coding (VVC) in-loop filtering (ILF) enhancement works mainly focus on learning the one-to-one mapping between the reconstructed and the original video frame, ignoring ... 详细信息
来源: 评论
Deep Image Compression with Latent Optimization and Piece-wise Quantization Approximation
Deep Image Compression with Latent Optimization and Piece-wi...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Wu, Yuyang Qi, Zhiyang Zheng, Huiming Tao, Lvfang Gao, Wei Peking Univ Sch Elect & Comp Engn Shenzhen Grad Sch Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China
Benefit from its capability of learning high-dimensional compact representation from raw data, the auto-encoders are widely used in various tasks of data compression. In particular, for deep image compression, auto-en... 详细信息
来源: 评论
Perceptual Loss for Robust Unsupervised Homography Estimation
Perceptual Loss for Robust Unsupervised Homography Estimatio...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Koguciuk, Daniel Arani, Elahe Zonooz, Bahram NavInfo Europe Adv Res Lab Eindhoven Netherlands
Homography estimation is often an indispensable step in many computer vision tasks. The existing approaches, however, are not robust to illumination and/or larger viewpoint changes. In this paper, we propose bidirecti... 详细信息
来源: 评论
Multi-task Learning with Attention for End-to-end Autonomous Driving
Multi-task Learning with Attention for End-to-end Autonomous...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Ishihara, Keishi Kanervisto, Anssi Miura, Jun Hautamaki, Ville Toyohashi Univ Technol Toyohashi Aichi Japan Univ Eastern Finland Kuopio Finland
Autonomous driving systems need to handle complex scenarios such as lane following, avoiding collisions, taking turns, and responding to traffic signals. In recent years, approaches based on end-to-end behavioral clon... 详细信息
来源: 评论
NTIRE 2022 Challenge on Learning the Super-Resolution Space
NTIRE 2022 Challenge on Learning the Super-Resolution Space
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Lugmayr, Andreas Danelljan, Martin Timofte, Radu Kim, Kang-wook Kim, Younggeun Lee, Jae-young Li, Zechao Pan, Jinshan Shim, Dongseok Song, Ki-Ung Tang, Jinhui Wang, Cong Zhao, Zhihao Swiss Fed Inst Technol Comp Vis Lab Zurich Switzerland
This paper reviews the NTIRE 2022 challenge on learning the super-Resolution space. This challenge aims to raise awareness that the super-resolution problem is ill-posed. Since many high-resolution images map to the s... 详细信息
来源: 评论
Generative Zero-shot Network Quantization
Generative Zero-shot Network Quantization
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: He, Xiangyu Lu, Jiahao Xu, Weixiang Hu, Qinghao Wang, Peisong Cheng, Jian Chinese Acad Sci Inst Automat Beijing Peoples R China
Convolutional neural networks are able to learn realistic image priors from numerous training samples in low-level image generation and restoration [66]. We show that, for high-level image recognition tasks, we can fu... 详细信息
来源: 评论
An Effective Temporal Localization Method with Multi-View 3D Action recognition for Untrimmed Naturalistic Driving Videos
An Effective Temporal Localization Method with Multi-View 3D...
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Manh Tung Tran Minh Quan Vu Ngoc Duong Hoang Khac-Hoai Nam Bui Viettel Grp Viettel Cyperspace Ctr Hanoi Vietnam
Naturalistic driving studies with computer vision techniques have become an emergent research issue. The objective is to classify the distracted behavior actions by drivers. Specifically, this issue is regarded as tem... 详细信息
来源: 评论
CL-Gym: Full-Featured PyTorch Library for Continual Learning
CL-Gym: Full-Featured PyTorch Library for Continual Learning
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Mirzadeh, Seyed Iman Ghasemzadeh, Hassan Washington State Univ Pullman WA 99164 USA
Continual learning (CL) has become one of the most active research venues within the artificial intelligence community in recent years. Given the significant amount of attention paid to continual learning, the need fo... 详细信息
来源: 评论
Deep Video Codec Control for vision Models
Deep Video Codec Control for Vision Models
收藏 引用
ieee/cvf conference on computer vision and pattern recognition (CVPR)
作者: Reich, Christoph Debnath, Biplob Patel, Deep Prangemeier, Tim Cremers, Daniel Chakradhar, Srimat NEC Labs Amer Inc San Jose CA 95110 USA Tech Univ Munich Munich Germany Tech Univ Darmstadt Darmstadt Germany Munich Ctr Machine Learning MCML Munich Germany
Standardized lossy video coding is at the core of almost all real-world video processing pipelines. Rate control is used to enable standard codecs to adapt to different network bandwidth conditions or storage constrai... 详细信息
来源: 评论