咨询与建议

限定检索结果

文献类型

  • 186 篇 会议
  • 111 篇 期刊文献

馆藏范围

  • 297 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 213 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
    • 8 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

  • 290 篇 英文
  • 6 篇 其他
  • 1 篇 中文
检索条件"机构=Computer Vision and Pattern Recognition Lab."
297 条 记 录,以下是61-70 订阅
排序:
Reflash Dropout in Image Super-Resolution
arXiv
收藏 引用
arXiv 2021年
作者: Kong, Xiangtao Liu, Xina Gu, Jinjin Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China University of Chinese Academy of Sciences China The University of Sydney Australia Shanghai AI Laboratory Shanghai China
Dropout is designed to relieve the overfitting problem in high-level vision tasks but is rarely applied in low-level vision tasks, like image super-resolution (SR). As a classic regression problem, SR exhibits a diffe... 详细信息
来源: 评论
Interactive Multi-dimension Modulation with Dynamic Controllab.e Residual Learning for Image Restoration  1
收藏 引用
16th European Conference on computer vision, ECCV 2020
作者: He, Jingwen Dong, Chao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China
Interactive image restoration aims to generate restored images by adjusting a controlling coefficient which determines the restoration level. Previous works are restricted in modulating image with a single coefficient... 详细信息
来源: 评论
Dual-AI: Dual-path Actor Interaction Learning for Group Activity recognition
arXiv
收藏 引用
arXiv 2022年
作者: Han, Mingfei Zhang, David Junhao Wang, Yali Yan, Rui Yao, Lina Chang, Xiaojun Qiao, Yu ReLER AAII UTS United States National University of Singapore Singapore ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China RMIT University Australia University of New South Wales Australia Shanghai AI Laboratory Shanghai China
Learning spatial-temporal relation among multiple actors is crucial for group activity recognition. Different group activities often show the diversified interactions between actors in the video. Hence, it is often di... 详细信息
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
Visual Compositional Learning for Human-Object Interaction Detection  16th
Visual Compositional Learning for Human-Object Interaction D...
收藏 引用
16th European Conference on computer vision, ECCV 2020
作者: Hou, Zhi Peng, Xiaojiang Qiao, Yu Tao, Dacheng UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China
Human-Object interaction (HOI) detection aims to localize and infer relationships between human and objects in an image. It is challenging because an enormous number of possible combinations of objects and verbs types... 详细信息
来源: 评论
Attention-Driven Dynamic Graph Convolutional Network for Multi-lab.l Image recognition  16th
Attention-Driven Dynamic Graph Convolutional Network for Mul...
收藏 引用
16th European Conference on computer vision, ECCV 2020
作者: Ye, Jin He, Junjun Peng, Xiaojiang Wu, Wenhao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China School of Biomedical Engineering the Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China
Recent studies often exploit Graph Convolutional Network (GCN) to model lab.l dependencies to improve recognition accuracy for multi-lab.l image recognition. However, constructing a graph by counting the lab.l co-occu... 详细信息
来源: 评论
Enhanced Quadratic Video Interpolation  16th
Enhanced Quadratic Video Interpolation
收藏 引用
Workshops held at the 16th European Conference on computer vision, ECCV 2020
作者: Liu, Yihao Xie, Liangbin Siyao, Li Sun, Wenxiu Qiao, Yu Dong, Chao ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China SenseTime Research Beijing China
With the prosperity of digital video industry, video frame interpolation has arisen continuous attention in computer vision community and become a new upsurge in industry. Many learning-based methods have been propose... 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
Digging into uncertainty in self-supervised multi-view stereo
arXiv
收藏 引用
arXiv 2021年
作者: Xu, Hongbin Zhou, Zhipeng Wang, Yali Kang, Wenxiong Sun, Baigui Li, Hao Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences South China University of Technology Shanghai AI Laboratory Alibaba Group Pazhou 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