咨询与建议

限定检索结果

文献类型

  • 22,771 篇 会议
  • 112 篇 期刊文献
  • 23 册 图书

馆藏范围

  • 22,905 篇 电子文献
  • 1 种 纸本馆藏

日期分布

学科分类号

  • 13,398 篇 工学
    • 10,880 篇 计算机科学与技术...
    • 3,450 篇 软件工程
    • 2,430 篇 机械工程
    • 1,721 篇 光学工程
    • 1,010 篇 控制科学与工程
    • 998 篇 电气工程
    • 761 篇 信息与通信工程
    • 393 篇 仪器科学与技术
    • 337 篇 生物工程
    • 257 篇 生物医学工程(可授...
    • 215 篇 电子科学与技术(可...
    • 113 篇 化学工程与技术
    • 112 篇 安全科学与工程
    • 98 篇 测绘科学与技术
    • 92 篇 交通运输工程
    • 86 篇 建筑学
    • 82 篇 土木工程
  • 3,362 篇 医学
    • 3,348 篇 临床医学
    • 79 篇 基础医学(可授医学...
  • 3,250 篇 理学
    • 1,953 篇 物理学
    • 1,664 篇 数学
    • 567 篇 统计学(可授理学、...
    • 484 篇 生物学
    • 245 篇 系统科学
    • 109 篇 化学
  • 506 篇 管理学
    • 299 篇 图书情报与档案管...
    • 219 篇 管理科学与工程(可...
    • 75 篇 工商管理
  • 252 篇 艺术学
    • 252 篇 设计学(可授艺术学...
  • 62 篇 法学
    • 59 篇 社会学
  • 40 篇 农学
  • 25 篇 教育学
  • 19 篇 经济学
  • 11 篇 军事学
  • 3 篇 文学

主题

  • 10,126 篇 computer vision
  • 4,025 篇 pattern recognit...
  • 2,900 篇 training
  • 1,958 篇 computational mo...
  • 1,792 篇 cameras
  • 1,758 篇 visualization
  • 1,485 篇 shape
  • 1,466 篇 image segmentati...
  • 1,447 篇 feature extracti...
  • 1,412 篇 three-dimensiona...
  • 1,288 篇 robustness
  • 1,169 篇 computer archite...
  • 1,144 篇 layout
  • 1,142 篇 computer science
  • 1,134 篇 semantics
  • 1,071 篇 object detection
  • 1,043 篇 conferences
  • 1,009 篇 benchmark testin...
  • 967 篇 codes
  • 810 篇 face recognition

机构

  • 135 篇 univ sci & techn...
  • 118 篇 univ chinese aca...
  • 118 篇 chinese univ hon...
  • 110 篇 carnegie mellon ...
  • 99 篇 tsinghua univers...
  • 99 篇 microsoft resear...
  • 94 篇 swiss fed inst t...
  • 92 篇 zhejiang univ pe...
  • 82 篇 university of sc...
  • 81 篇 zhejiang univers...
  • 77 篇 shanghai ai lab ...
  • 77 篇 university of ch...
  • 72 篇 shanghai jiao to...
  • 68 篇 microsoft res as...
  • 65 篇 national laborat...
  • 65 篇 alibaba grp peop...
  • 64 篇 tsinghua univ pe...
  • 63 篇 adobe research
  • 60 篇 peking univ peop...
  • 59 篇 peng cheng labor...

作者

  • 78 篇 van gool luc
  • 72 篇 timofte radu
  • 63 篇 zhang lei
  • 45 篇 luc van gool
  • 40 篇 yang yi
  • 37 篇 loy chen change
  • 33 篇 xiaoou tang
  • 33 篇 li stan z.
  • 33 篇 qi tian
  • 32 篇 sun jian
  • 31 篇 liu yang
  • 31 篇 li fei-fei
  • 30 篇 chen chen
  • 30 篇 tian qi
  • 30 篇 pascal fua
  • 29 篇 darrell trevor
  • 28 篇 ying shan
  • 27 篇 li xin
  • 27 篇 vasconcelos nuno
  • 27 篇 hanqing lu

语言

  • 22,844 篇 英文
  • 35 篇 其他
  • 20 篇 中文
  • 5 篇 土耳其文
  • 2 篇 日文
检索条件"任意字段=1994 IEEE Computer-Society Conference on Computer Vision and Pattern Recognition"
22906 条 记 录,以下是4741-4750 订阅
排序:
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Meshes with Biharmonic Coordinates
DeepMetaHandles: Learning Deformation Meta-Handles of 3D Mes...
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Liu, Minghua Sung, Minhyuk Mech, Radomir Su, Hao Univ Calif San Diego San Diego CA 92103 USA Korea Adv Inst Sci & Technol Daejeon South Korea Adobe Res San Jose CA USA
We propose DeepMetaHandles, a 3D conditional generative model based on mesh deformation. Given a collection of 3D meshes of a category and their deformation handles (control points), our method learns a set of metahan... 详细信息
来源: 评论
HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms
HistoGAN: Controlling Colors of GAN-Generated and Real Image...
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Afifi, Mahmoud Brubaker, Marcus A. Brown, Michael S. York Univ N York ON Canada
While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical for their adoption in graphic design an... 详细信息
来源: 评论
Test of Time: Instilling Video-Language Models with a Sense of Time
Test of Time: Instilling Video-Language Models with a Sense ...
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Bagad, Piyush Tapaswi, Makarand Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands IIIT Hyderabad Hyderabad India
Modelling and understanding time remains a challenge in contemporary video understanding models. With language emerging as a key driver towards powerful generalization, it is imperative for foundational video-language... 详细信息
来源: 评论
Adversarial Feature Augmentation for Unsupervised Domain Adaptation  31
Adversarial Feature Augmentation for Unsupervised Domain Ada...
收藏 引用
31st ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Volpi, Riccardo Morerio, Pietro Savarese, Silvio Murino, Vittorio Ist Italiano Tecnol Pattern Anal & Comp Vis Genoa Italy Stanford Univ Stanford Vis & Learning Lab Stanford CA 94305 USA Univ Verona Comp Sci Dept Verona Italy
Recent works showed that Generative Adversarial Networks (GANs) can be successfully applied in unsupervised domain adaptation, where, given a labeled source dataset and an unlabeled target dataset, the goal is to trai... 详细信息
来源: 评论
Visual inspection of machined parts
Visual inspection of machined parts
收藏 引用
1992 ieee computer society conference on computer vision and pattern recognition, CVPR 1992
作者: Modayur, B.R. Shapiro, L.G. Haralick, R.M. Department of Electrical Engineering FT -10 University of Washington SeattleWA98195 United States Department of Computer Science and Engineering FR-35 University of Washington SeattleWA98195 United States
A CAD-model-based machine vision system for dimensional inspection of machine parts is described, with emphasis on the theory behind the system. The original contributions of this work are: (1) the use of precise defi... 详细信息
来源: 评论
Scene Graph Generation with External Knowledge and Image Reconstruction  32
Scene Graph Generation with External Knowledge and Image Rec...
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Gu, Jiuxiang Zhao, Handong Lin, Zhe Li, Sheng Cai, Jianfei Ling, Mingyang Nanyang Technol Univ Interdisciplinary Grad Sch ROSE Lab Singapore Singapore Adobe Res San Jose CA USA Univ Georgia Athens GA 30602 USA Google Cloud AI San Jose CA USA
Scene graph generation has received growing attention with the advancements in image understanding tasks such as object detection, attributes and relationship prediction, etc. However, existing datasets are biased in ... 详细信息
来源: 评论
Using Unknown Occluders to Recover Hidden Scenes  32
Using Unknown Occluders to Recover Hidden Scenes
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Yedidia, Adam B. Baradad, Manel Thrampoulidis, Christos Freeman, William T. Wornell, Gregory W. MIT Cambridge MA 02139 USA UC Santa Barbara Santa Barbara CA USA Google Res Mountain View CA USA
We consider the challenging problem of inferring a hidden moving scene from faint shadows cast on a diffuse surface. Recent work in passive non-line-of-sight (NLoS) imaging has shown that the presence of occluding obj... 详细信息
来源: 评论
A-CNN: Annularly Convolutional Neural Networks on Point Clouds  32
A-CNN: Annularly Convolutional Neural Networks on Point Clou...
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Komarichev, Artem Zhong, Zichun Hua, Jing Wayne State Univ Dept Comp Sci Detroit MI 48202 USA
Analyzing the geometric and semantic properties of 3D point clouds through the deep networks is still challenging due to the irregularity and sparsity of samplings of their geometric structures. This paper presents a ... 详细信息
来源: 评论
NightLab: A Dual-level Architecture with Hardness Detection for Segmentation at Night
NightLab: A Dual-level Architecture with Hardness Detection ...
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Deng, Xueqing Wang, Peng Lian, Xiaochen Newsam, Shawn Univ Calif Merced EECS Merced CA 95343 USA ByteDance Inc Beijing Peoples R China
The semantic segmentation of nighttime scenes is a challenging problem that is key to impactful applications like self-driving cars. Yet, it has received little attention compared to its daytime counterpart. In this p... 详细信息
来源: 评论
Low-Resource vision Challenges for Foundation Models
Low-Resource Vision Challenges for Foundation Models
收藏 引用
ieee/CVF conference on computer vision and pattern recognition (CVPR)
作者: Zhang, Yunhua Doughty, Hazel Snoek, Cees G. M. Univ Amsterdam Amsterdam Netherlands Leiden Univ Leiden Netherlands
Low-resource settings are well-established in natural language processing, where many languages lack sufficient data for deep learning at scale. However, low-resource problems are under-explored in computer vision. In...
来源: 评论