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131-140 订阅
排序:
nnDetection: A Self-configuring Method for Medical Object Detection
arXiv
收藏 引用
arXiv 2021年
作者: Baumgartner, Michael Jäger, Paul F. Isensee, Fabian Maier-Hein, Klaus H. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Interactive Machine Learning Group German Cancer Research Center Germany HIP Applied Computer Vision Lab. German Cancer Research Center Germany Pattern Analysis and Learning Group Heidelberg University Hospital Germany
Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions often depend on rating of objects rat... 详细信息
来源: 评论
Visual compositional learning for Human-Object interaction detection
arXiv
收藏 引用
arXiv 2020年
作者: Hou, Zhi Peng, Xiaojiang Qiao, Yu Tao, Dacheng UBTECH Sydney AI Centre School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences 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
ICDAR 2019 CROHME + TFD: Competition on recognition of handwritten mathematical expressions and typeset formula detection  15
ICDAR 2019 CROHME + TFD: Competition on recognition of handw...
收藏 引用
15th IAPR International Conference on Document Analysis and recognition, ICDAR 2019
作者: Mahdavi, Mahshad Zanibbi, Richard Mouchere, Harold Viard-Gaudin, Christian Garain, Utpal Document and Pattern Recognition Lab Rochester Institute of Technology RochesterNY United States Christian Viard-Gaudin LS2N-UMR CNRS 6004 University of Nantes Nantes France Computer Vision and Pattern Recognition Unit Centre for Artif. Intel. and Mach. Leaning Indian Statistical Institute Kolkata India
We summarize the tasks, protocol, and outcome for the 6th Competition on recognition of Handwritten Mathematical Expressions (CROHME), which includes a new formula detection in document images task (+ TFD). For CROHME... 详细信息
来源: 评论
EfficientFCN: Holistically-guided decoding for semantic segmentation
arXiv
收藏 引用
arXiv 2020年
作者: Liu, Jianbo He, Junjun Zhang, Jiawei Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory Chinese University of Hong Kong Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SenseTime Research China
Both performance and efficiency are important to semantic segmentation. State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated conv... 详细信息
来源: 评论
Attention-driven dynamic graph convolutional network for multi-lab.l image recognition
arXiv
收藏 引用
arXiv 2020年
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 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
TTPP: Temporal transformer with progressive prediction for efficient action anticipation
arXiv
收藏 引用
arXiv 2020年
作者: Wang, Wen Peng, Xiaojiang Su, Yanzhou Qiao, Yu Cheng, Jian School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu Sichuan611731 China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab. Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society
Video action anticipation aims to predict future action categories from observed frames. Current state-of-the-art approaches mainly resort to recurrent neural networks to encode history information into hidden states,... 详细信息
来源: 评论
A learning-based method for online adjustment of C-arm cone-beam CT source trajectories for artifact avoidance
arXiv
收藏 引用
arXiv 2020年
作者: Thies, Mareike Zäch, Jan-Nico Gao, Cong Taylor, Russell Navab, Nassir Maier, Andreas Unberath, Mathias Laboratory for Computational Sensing + Robotics Johns Hopkins University BaltimoreMD United States Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Computer Vision Laboratory Eidgenössische Technische Hochschule Zürich
Purpose: During spinal fusion surgery, screws are placed close to critical nerves suggesting the need for highly accurate screw placement. Verifying screw placement on high-quality tomographic imaging is essential. C-... 详细信息
来源: 评论
Tensor Low-Rank Reconstruction for Semantic Segmentation
arXiv
收藏 引用
arXiv 2020年
作者: Chen, Wanli Zhu, Xinge Sun, Ruoqi He, Junjun Li, Ruiyu Shen, Xiaoyong Yu, Bei Chinese University of Hong Kong Hong Kong Shanghai Jiao Tong University China ShenZhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China SmartMore United States
Context information plays an indispensable role in the success of semantic segmentation. Recently, non-local self-attention based methods are proved to be effective for context information collection. Since the desire... 详细信息
来源: 评论
NiuEM: A Nested-iterative Unsupervised Learning Model for Single-particle Cryo-EM Image Processing
NiuEM: A Nested-iterative Unsupervised Learning Model for Si...
收藏 引用
2020 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2020
作者: Hu, Rui Cai, Jiaming Zheng, Wangjie Yang, Yang Shen, Hong-Bin Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Jiao Tong University Department of Bioinformatics and Biostatistics Shanghai200240 China
Cryo-electron microscopy (cryo-EM) has become a mainstream technology for solving spatial structures of biomacromolecules, while the processing of cryo-EM images is a very challenging task. One of the great challenges... 详细信息
来源: 评论
Conditional sequential modulation for efficient global image retouching
arXiv
收藏 引用
arXiv 2020年
作者: He, Jingwen Liu, Yihao 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 SIAT Branch Shenzhen Institute of Artificial Intelligence Robotics for Society Korea Republic of University of Chinese Academy of Sciences China
Photo retouching aims at enhancing the aesthetic visual quality of images that suffer from photographic defects such as over/under exposure, poor contrast, inharmonious saturation. Practically, photo retouching can be... 详细信息
来源: 评论