咨询与建议

限定检索结果

文献类型

  • 186 篇 会议
  • 112 篇 期刊文献
  • 1 册 图书

馆藏范围

  • 299 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 214 篇 工学
    • 141 篇 计算机科学与技术...
    • 132 篇 软件工程
    • 59 篇 信息与通信工程
    • 41 篇 光学工程
    • 30 篇 生物工程
    • 25 篇 生物医学工程(可授...
    • 24 篇 控制科学与工程
    • 20 篇 机械工程
    • 10 篇 化学工程与技术
    • 9 篇 电子科学与技术(可...
    • 7 篇 仪器科学与技术
    • 7 篇 电气工程
    • 6 篇 建筑学
    • 5 篇 安全科学与工程
    • 4 篇 力学(可授工学、理...
    • 4 篇 材料科学与工程(可...
    • 4 篇 土木工程
    • 4 篇 交通运输工程
  • 136 篇 理学
    • 61 篇 数学
    • 56 篇 物理学
    • 32 篇 生物学
    • 13 篇 统计学(可授理学、...
    • 11 篇 化学
    • 8 篇 系统科学
  • 56 篇 管理学
    • 41 篇 图书情报与档案管...
    • 17 篇 管理科学与工程(可...
  • 10 篇 医学
    • 9 篇 临床医学
    • 8 篇 基础医学(可授医学...
    • 8 篇 药学(可授医学、理...
  • 8 篇 法学
    • 8 篇 社会学
  • 3 篇 艺术学
  • 2 篇 教育学
  • 1 篇 文学

主题

  • 17 篇 feature extracti...
  • 15 篇 image segmentati...
  • 15 篇 convolution
  • 13 篇 semantics
  • 12 篇 image reconstruc...
  • 11 篇 computer vision
  • 10 篇 image edge detec...
  • 9 篇 image color anal...
  • 8 篇 face recognition
  • 7 篇 generative adver...
  • 7 篇 three-dimensiona...
  • 7 篇 face
  • 7 篇 training
  • 6 篇 pixels
  • 6 篇 shape
  • 5 篇 writing
  • 5 篇 pattern recognit...
  • 4 篇 image enhancemen...
  • 4 篇 support vector m...
  • 4 篇 semantic segment...

机构

  • 40 篇 university of ch...
  • 40 篇 shenzhen key lab...
  • 31 篇 national key lab...
  • 31 篇 computer vision ...
  • 26 篇 shenzhen key lab...
  • 22 篇 faculty of compu...
  • 21 篇 siat branch shen...
  • 19 篇 shanghai ai labo...
  • 16 篇 sensetime resear...
  • 16 篇 shenzhen key lab...
  • 11 篇 shanghai artific...
  • 8 篇 shanghai ai lab
  • 8 篇 the chinese univ...
  • 7 篇 department of st...
  • 7 篇 the university o...
  • 6 篇 shanghai jiao to...
  • 6 篇 shenzhen key lab...
  • 6 篇 university of ma...
  • 6 篇 guangzhou power ...
  • 5 篇 arc lab tencent ...

作者

  • 59 篇 qiao yu
  • 27 篇 yu qiao
  • 27 篇 dong chao
  • 19 篇 pal umapada
  • 17 篇 umapada pal
  • 17 篇 wang yali
  • 17 篇 lu tong
  • 16 篇 tong lu
  • 16 篇 palaiahnakote sh...
  • 15 篇 maier andreas
  • 15 篇 shivakumara pala...
  • 11 篇 chao dong
  • 10 篇 he junjun
  • 9 篇 chen xiangyu
  • 9 篇 gu jinjin
  • 9 篇 peng xiaojiang
  • 8 篇 chen shifeng
  • 8 篇 ren jimmy s.
  • 7 篇 blumenstein mich...
  • 7 篇 zhou zhipeng

语言

  • 292 篇 英文
  • 6 篇 其他
  • 1 篇 中文
检索条件"机构=Computer Vision and Pattern Recognition Lab."
299 条 记 录,以下是171-180 订阅
排序:
Learning to predict context-adaptive convolution for semantic segmentation
arXiv
收藏 引用
arXiv 2020年
作者: Liu, Jianbo He, Junjun Ren, Jimmy S. Qiao, Yu Li, Hongsheng CUHK-SenseTime Joint Laboratory Chinese University of Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SenseTime Research
Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods [34] demonstrate that using global context for re-weighting feature channels c... 详细信息
来源: 评论
DegAE: A New Pretraining Paradigm for Low-Level vision
DegAE: A New Pretraining Paradigm for Low-Level Vision
收藏 引用
Conference on computer vision and pattern recognition (CVPR)
作者: Yihao Liu Jingwen He Jinjin Gu Xiangtao Kong Yu Qiao Chao Dong Shanghai Artificial Intelligence Laboratory ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of Chinese Academy of Sciences The University of Sydney
Self-supervised pretraining has achieved remarkable success in high-level vision, but its application in low-level vision remains ambiguous and not well-established. What is the primitive intention of pretraining? Wha...
来源: 评论
Learning dynamical human-joint affinity for 3D pose estimation in videos
arXiv
收藏 引用
arXiv 2021年
作者: Zhang, Junhao Wang, Yali Zhou, Zhipeng Luan, Tianyu Wang, Zhe Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences University of California Irvine United States Shanghai AI Laboratory Shanghai China
Graph Convolution Network (GCN) has been successfully used for 3D human pose estimation in videos. However, it is often built on the fixed human-joint affinity, according to human skeleton. This may reduce adaptation ... 详细信息
来源: 评论
Compressed sensing ensemble classifier for human detection
Compressed sensing ensemble classifier for human detection
收藏 引用
4th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2013
作者: Zhang, Baochang Liu, Juan Gao, Yongsheng Liu, Jianzhuang Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering BeiHang University Beijing 100191 China School of Engineering Griffith University Australia Shenzhen Key Lab for Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Department of Information Engineering Chinese University of Hong Kong Hong Kong Hong Kong
This paper proposes a novel Compressed Sensing Ensemble Classifier (CSEC) for human detection. The proposed CSEC employs the compressed sensing technique to get a more sparse model with a more reasonable selection of ... 详细信息
来源: 评论
Color constancy and non-uniform illumination: Can existing algorithms work?
Color constancy and non-uniform illumination: Can existing a...
收藏 引用
International Conference on computer vision Workshops (ICCV Workshops)
作者: Michael Bleier Christian Riess Shida Beigpour Eva Eibenberger Elli Angelopoulou Tobias Tröger André Kaup Pattern Recognition Lab University of Erlangen-Nuremberg Germany Computer Vision Center Universidad Autónoma de Barcelona Spain Multimedia Communications and Signal Processing University of Erlangen-Nuremberg Germany
The color and distribution of illuminants can significantly alter the appearance of a scene. The goal of color constancy (CC) is to remove the color bias introduced by the illuminants. Most existing CC algorithms assu... 详细信息
来源: 评论
Multiple Transfer Learning and Multi-lab.l Balanced Training Strategies for Facial AU Detection In the Wild
Multiple Transfer Learning and Multi-label Balanced Training...
收藏 引用
IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)
作者: Sijie Ji Kai Wang Xiaojiang Peng Jianfei Yang Zhaoyang Zeng Yu Qiao Nanyang Technological University Singapore ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Science Sun Yat-Sen University China
This paper 1 presents SIAT-NTU solution and results of facial action unit (AU) detection in the EmotiNet Challenge 2020. The task aims to detect 23 AUs from facial images in the wild, and its main difficulties lie in... 详细信息
来源: 评论
Digging into Uncertainty in Self-supervised Multi-view Stereo
Digging into Uncertainty in Self-supervised Multi-view Stere...
收藏 引用
International Conference on computer vision (ICCV)
作者: Hongbin Xu Zhipeng Zhou Yali Wang Wenxiong Kang Baigui Sun Hao Li Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences South China University of Technology Alibaba Group Pazhou Laboratory Shanghai AI Laboratory
Self-supervised Multi-view stereo (MVS) with a pretext task of image reconstruction has achieved significant progress recently. However, previous methods are built upon intuitions, lacking comprehensive explanations a... 详细信息
来源: 评论
Self-supervised multi-view stereo via effective co-segmentation and data-augmentation
arXiv
收藏 引用
arXiv 2021年
作者: Xu, Hongbin Zhou, Zhipeng Qiao, Yu Kang, Wenxiong Wu, Qiuxia ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shanghai AI Lab Shanghai China South China University of Technology Guangzhou China
Recent studies have witnessed that self-supervised methods based on view synthesis obtain clear progress on multiview stereo (MVS). However, existing methods rely on the assumption that the corresponding points among ... 详细信息
来源: 评论
Suppressing Uncertainties for Large-Scale Facial Expression recognition
Suppressing Uncertainties for Large-Scale Facial Expression ...
收藏 引用
Conference on computer vision and pattern recognition (CVPR)
作者: Kai Wang Xiaojiang Peng Jianfei Yang Shijian Lu Yu Qiao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Science University of Chinese Academy of Sciences China Nanyang Technological University Singapore
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. T... 详细信息
来源: 评论
Suppressing uncertainties for large-scale facial expression recognition
arXiv
收藏 引用
arXiv 2020年
作者: Wang, Kai Peng, Xiaojiang Yang, Jianfei Lu, Shijian Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Science University of Chinese Academy of Sciences China Nanyang Technological University Singapore
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the uncertainties caused by ambiguous facial expressions, low-quality facial images, and the subjectiveness of annotators. T... 详细信息
来源: 评论