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1-10 订阅
排序:
Blind Image Inpainting via Omni-dimensional Gated Attention and Wavelet Queries
Blind Image Inpainting via Omni-dimensional Gated Attention ...
收藏 引用
2023 IEEE/CVF Conference on computer vision and pattern recognition Workshops, CVPRW 2023
作者: Phutke, Shruti S. Kulkarni, Ashutosh Vipparthi, Santosh Kumar Murala, Subrahmanyam Indian Institute of Technology Ropar Computer Vision and Pattern Recognition Lab Punjab Rupnagar India
Blind image inpainting is a crucial restoration task that does not demand additional mask information to restore the corrupted regions. Yet, it is a very less explored research area due to the difficulty in discrimina... 详细信息
来源: 评论
Bootstrap Diffusion Model Curve Estimation for High Resolution Low-Light Image Enhancement  20th
Bootstrap Diffusion Model Curve Estimation for High Resolut...
收藏 引用
20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023
作者: Huang, Jiancheng Liu, Yifan Chen, Shifeng ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China University of Chinese Academy of Sciences Beijing China
Learning-based methods have attracted a lot of research attention and led to significant improvements in low-light image enhancement. However, most of them still suffer from two main problems: expensive computational ... 详细信息
来源: 评论
Dynamic Feature Queue for Surveillance Face Anti-spoofing via Progressive Training
Dynamic Feature Queue for Surveillance Face Anti-spoofing vi...
收藏 引用
2023 IEEE/CVF Conference on computer vision and pattern recognition Workshops, CVPRW 2023
作者: Wang, Keyao Huang, Mouxiao Zhang, Guosheng Yue, Haixiao Zhang, Gang Qiao, Yu China Chinese Academy of Sciences ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology China University of Chinese Academy of Sciences China
In recent years, face recognition systems have faced increasingly security threats, making it essential to employ Face Anti-spoofing (FAS) to protect against various types of attacks in traditional scenarios like phon... 详细信息
来源: 评论
Generalized Multimodal Fusion via Poisson-Nernst-Planck Equation
arXiv
收藏 引用
arXiv 2024年
作者: Xiong, Jiayu Wang, Jing Xiang, Hengjing Xue, Jun Xu, Chen Jiang, Zhouqiang Xiamen Key Lab of Computer Vision and Pattern Recognition Huaqiao University Xiamen China
Previous studies have highlighted significant advancements in multimodal fusion. Nevertheless, such methods often encounter challenges regarding the efficacy of feature extraction, data integrity, consistency of featu... 详细信息
来源: 评论
Generalist Segmentation Algorithm for Photoreceptors Analysis in Adaptive Optics Imaging  27th
Generalist Segmentation Algorithm for Photoreceptors Analys...
收藏 引用
27th International Conference on pattern recognition, ICPR 2024
作者: Kulyabin, Mikhail Sindel, Aline Pedersen, Hilde R. Gilson, Stuart Baraas, Rigmor Maier, Andreas Pattern Recognition Lab Department of Computer Science Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany National Centre for Optics Vision and Eye Care Faculty of Health and Social Sciences University of South-Eastern Norway Kongsberg Norway
Analyzing the cone photoreceptor pattern in images obtained from the living human retina using quantitative methods can be crucial for the early detection and management of various eye conditions. Confocal adaptive op... 详细信息
来源: 评论
DarkGAN: Night Image Enhancement Using Generative Adversarial Networks  5th
DarkGAN: Night Image Enhancement Using Generative Adversaria...
收藏 引用
5th International Conference on computer vision and Image Processing, CVIP 2020
作者: Alaspure, Prasen Hambarde, Praful Dudhane, Akshay Murala, Subrahmanyam Computer Vision and Pattern Recognition Lab IIT Ropar Rupnagar India
Low light image enhancement is one of the challenging tasks in computer vision, and it becomes more difficult when images are very dark. Recently, most of low light image enhancement work is done either on synthetic d... 详细信息
来源: 评论
Blind Image Inpainting via Omni-dimensional Gated Attention and Wavelet Queries
Blind Image Inpainting via Omni-dimensional Gated Attention ...
收藏 引用
IEEE computer Society Conference on computer vision and pattern recognition Workshops (CVPRW)
作者: Shruti S. Phutke Ashutosh Kulkarni Santosh Kumar Vipparthi Subrahmanyam Murala Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Rupnagar Punjab
Blind image inpainting is a crucial restoration task that does not demand additional mask information to restore the corrupted regions. Yet, it is a very less explored research area due to the difficulty in discrimina...
来源: 评论
Ensemble and Personalized Transformer Models for Subject Identification and Relapse Detection in E-Prevention Challenge
Ensemble and Personalized Transformer Models for Subject Ide...
收藏 引用
International Conference on Acoustics, Speech, and Signal Processing (ICASSP)
作者: Salvatore Calcagno Raffaele Mineo Daniela Giordano Concetto Spampinato Department of Electrical Electronics and Computer Engineering Pattern Recognition and Computer Vision Laboratory (PeRCeiVe Lab) University of Catania Italy
In this short paper, we present the devised solutions for the subject identification and relapse detection tasks, which are part of the e-Prevention Challenge hosted at the ICASSP 2023 conference [1] [2] [3]. We speci... 详细信息
来源: 评论
Finding discriminative filters for specific degradations in blind super-resolution  21
Finding discriminative filters for specific degradations in ...
收藏 引用
Proceedings of the 35th International Conference on Neural Information Processing Systems
作者: Liangbin Xie Xintao Wang Chao Dong Zhongang Qi Ying Shan Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences and University of Chinese Academy of Sciences and ARC Lab Tencent PCG ARC Lab Tencent PCG Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences and Shanghai AI Laboratory Shanghai China
Recent blind super-resolution (SR) methods typically consist of two branches, one for degradation prediction and the other for conditional restoration. However, our experiments show that a one-branch network can achie...
来源: 评论
Efficient Image Super-Resolution Using Vast-Receptive-Field Attention  17th
Efficient Image Super-Resolution Using Vast-Receptive-Field ...
收藏 引用
17th European Conference on computer vision, ECCV 2022
作者: Zhou, Lin Cai, Haoming Gu, Jinjin Li, Zheyuan Liu, Yingqi Chen, Xiangyu 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 Shanghai AI Laboratory Shanghai China The University of Sydney Sydney Australia University of Macau Zhuhai China
The attention mechanism plays a pivotal role in designing advanced super-resolution (SR) networks. In this work, we design an efficient SR network by improving the attention mechanism. We start from a simple pixel att... 详细信息
来源: 评论