咨询与建议

限定检索结果

文献类型

  • 73 篇 会议
  • 62 篇 期刊文献

馆藏范围

  • 135 篇 电子文献
  • 0 种 纸本馆藏

日期分布

学科分类号

  • 103 篇 工学
    • 72 篇 计算机科学与技术...
    • 67 篇 软件工程
    • 33 篇 信息与通信工程
    • 23 篇 生物工程
    • 22 篇 光学工程
    • 11 篇 生物医学工程(可授...
    • 9 篇 机械工程
    • 8 篇 化学工程与技术
    • 6 篇 控制科学与工程
    • 4 篇 电气工程
    • 4 篇 建筑学
    • 4 篇 交通运输工程
    • 3 篇 仪器科学与技术
    • 3 篇 电子科学与技术(可...
    • 3 篇 土木工程
    • 3 篇 安全科学与工程
    • 2 篇 材料科学与工程(可...
    • 2 篇 测绘科学与技术
  • 63 篇 理学
    • 32 篇 物理学
    • 23 篇 生物学
    • 19 篇 数学
    • 8 篇 化学
    • 7 篇 统计学(可授理学、...
    • 1 篇 大气科学
  • 33 篇 管理学
    • 23 篇 图书情报与档案管...
    • 13 篇 管理科学与工程(可...
  • 6 篇 医学
    • 6 篇 临床医学
    • 5 篇 基础医学(可授医学...
    • 4 篇 药学(可授医学、理...
  • 2 篇 法学
    • 2 篇 社会学
  • 1 篇 农学

主题

  • 12 篇 convolution
  • 5 篇 deep learning
  • 5 篇 semantics
  • 4 篇 distillation
  • 4 篇 pixels
  • 3 篇 face recognition
  • 3 篇 computer vision
  • 3 篇 image reconstruc...
  • 3 篇 training
  • 2 篇 image enhancemen...
  • 2 篇 semantic segment...
  • 2 篇 hidden markov mo...
  • 2 篇 generative adver...
  • 2 篇 non-local
  • 2 篇 long short-term ...
  • 2 篇 image segmentati...
  • 2 篇 signal encoding
  • 2 篇 handwritten math...
  • 2 篇 vectors
  • 2 篇 data mining

机构

  • 23 篇 fujian key labor...
  • 19 篇 shanghai ai labo...
  • 18 篇 school of comput...
  • 17 篇 shenzhen key lab...
  • 14 篇 university of ch...
  • 12 篇 xiamen key labor...
  • 11 篇 college of compu...
  • 11 篇 sensetime resear...
  • 11 篇 shanghai artific...
  • 9 篇 shenzhen key lab...
  • 8 篇 department of co...
  • 8 篇 fujian key labor...
  • 7 篇 department of in...
  • 6 篇 shenzhen key lab...
  • 6 篇 the university o...
  • 6 篇 shenzhen key lab...
  • 6 篇 university of ma...
  • 5 篇 shenzhen key lab...
  • 5 篇 fujian key labor...
  • 5 篇 college of compu...

作者

  • 22 篇 qiao yu
  • 20 篇 wang da-han
  • 13 篇 wang yali
  • 10 篇 dong chao
  • 9 篇 zhu shunzhi
  • 9 篇 chen xiangyu
  • 8 篇 yu qiao
  • 7 篇 da-han wang
  • 6 篇 he junjun
  • 6 篇 weng wei
  • 6 篇 li hongsheng
  • 6 篇 chao dong
  • 6 篇 chen si
  • 5 篇 liu xin
  • 5 篇 gu jinjin
  • 5 篇 wu yun
  • 5 篇 zhang xu-yao
  • 5 篇 li kunchang
  • 5 篇 liu jianzhuang
  • 5 篇 ren jimmy s.

语言

  • 132 篇 英文
  • 2 篇 其他
  • 1 篇 中文
检索条件"机构=Xiamen Key Laboratory of Computer Vision and Pattern Recognition"
135 条 记 录,以下是91-100 订阅
排序:
Cross Domain Object Detection by Target-Perceived Dual Branch Distillation
arXiv
收藏 引用
arXiv 2022年
作者: He, Mengzhe Wang, Yali Wu, Jiaxi Wang, Yiru Li, Hanqing Li, Bo Gan, Weihao Wu, Wei Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China SenseTime Research University of Chinese Academy of Science China Shanghai AI Laboratory Shanghai China Beihang University China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China
Cross domain object detection is a realistic and challenging task in the wild. It suffers from performance degradation due to large shift of data distributions and lack of instance-level annotations in the target doma... 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 to Predict Context-Adaptive Convolution for Semantic Segmentation  16th
Learning to Predict Context-Adaptive Convolution for Semanti...
收藏 引用
16th European Conference on computer vision, ECCV 2020
作者: Liu, Jianbo He, Junjun Qiao, Yu Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The 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 Beijing China SenseTime Research Hong Kong
Long-range contextual information is essential for achieving high-performance semantic segmentation. Previous feature re-weighting methods demonstrate that using global context for re-weighting feature channels can ef... 详细信息
来源: 评论
Revisiting the Generalization Problem of Low-level vision Models Through the Lens of Image Deraining
arXiv
收藏 引用
arXiv 2025年
作者: Hu, Jinfan You, Zhiyuan Gu, Jinjin Zhu, Kaiwen Xue, Tianfan Dong, Chao Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China The Chinese University of Hong Kong 999077 Hong Kong The University of Sydney NSW2006 Australia Shanghai Jiao Tong University Shanghai200240 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Shenzhen University of Advanced Technology Shenzhen518055 China
Generalization remains a significant challenge for low-level vision models, which often struggle with unseen degradations in real-world scenarios despite their success in controlled benchmarks. In this paper, we revis... 详细信息
来源: 评论
EfficientFCN: Holistically-Guided Decoding for Semantic Segmentation  1
收藏 引用
16th European Conference on computer vision, ECCV 2020
作者: Liu, Jianbo He, Junjun Zhang, Jiawei Ren, Jimmy S. Li, Hongsheng CUHK-SenseTime Joint Laboratory The Chinese University of Hong Kong Shatin Hong Kong Shenzhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China SenseTime Research Beijing 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
VideoPipe 2022 Challenge: Real-World Video Understanding for Urban Pipe Inspection
arXiv
收藏 引用
arXiv 2022年
作者: Liu, Yi Zhang, Xuan Li, Ying Liang, Guixin Jiang, Yabing Qiu, Lixia Tang, Haiping Xie, Fei Yao, Wei Dai, Yi Qiao, Yu Wang, Yali ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences China Shenzhen Bwell Technology Co. Ltd China Shenzhen Longhua Drainage Co. Ltd China Shanghai AI Laboratory Shanghai China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society China
Video understanding is an important problem in computer vision. Currently, the well-studied task in this research is human action recognition, where the clips are manually trimmed from the long videos, and a single cl... 详细信息
来源: 评论
Face-sketch learning with human sketch-drawing order enforcement
收藏 引用
Science China(Information Sciences) 2020年 第11期63卷 298-311页
作者: Liang CHANG Lihua JIN Lifen WENG Wentao CHAO Xuguang WANG Xiaoming DENG Qiulei DONG School of Artificial Intelligence Beijing Normal University Department of Design Art Xiamen University of Technology Department of Automation North China Electric Power University Beijing Key Laboratory of Human Computer Interactions Institute of Software Chinese Academy of Sciences National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences
Dear editor,Although face-sketch synthesis generates a sketch from a given face photo automatically [1], it is an open research problem in computer vision [2–4]. Recently, several deep neural network (DNN)methods for... 详细信息
来源: 评论
Feature refinement: An expression-specific feature learning and fusion method for micro-expression recognition
arXiv
收藏 引用
arXiv 2021年
作者: Zhou, Ling Mao, Qirong Huang, Xiaohua Zhang, Feifei Zhang, Zhihong School of Computer Science and Communication Engineering Jiangsu University ZhenjiangJiangsu212013 China School of Computer Engineering Nanjing Institute of Technology China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China Xiamen University Xiamen China Center for Machine Vision and Signal Analysis University of Oulu Finland
Micro-Expression recognition has become challenging, as it is extremely difficult to extract the subtle facial changes of micro-expressions. Recently, several approaches proposed several expression-shared features alg... 详细信息
来源: 评论
UniFormer: Unifying Convolution and Self-attention for Visual recognition
arXiv
收藏 引用
arXiv 2022年
作者: Li, Kunchang Wang, Yali Zhang, Junhao Gao, Peng Song, Guanglu Liu, Yu Li, Hongsheng Qiao, Yu ShenZhen Key Lab of Computer Vision and Pattern Recognition Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Shanghai Artificial Intelligence Laboratory Shanghai200232 China National University of Singapore Singapore Shanghai Artificial Intelligence Laboratory China SenseTime Research China The Chinese University of Hong Kong Hong Kong
It is a challenging task to learn discriminative representation from images and videos, due to large local redundancy and complex global dependency in these visual data. Convolution neural networks (CNNs) and vision t... 详细信息
来源: 评论
Detach and Enhance: Learning Disentangled Cross-modal Latent Representation for Efficient Face-Voice Association and Matching
Detach and Enhance: Learning Disentangled Cross-modal Latent...
收藏 引用
IEEE International Conference on Data Mining (ICDM)
作者: Zhenning Yu Xin Liu Yiu-Ming Cheung Minghang Zhu Xing Xu Nannan Wang Taihao Li Dept. of Comput. Sci. & Fujian Key Lab. of Big Data Intelligence and Security Huaqiao University Xiamen China Zhejiang Lab Hangzhou China Dept. of Comput. Sci. and Institute of Research and Continuing Education HK Baptist University Hong Kong SAR China Xiamen Key Lab. of Computer Vision and Pattern Recognition Huaqiao University Xiamen China Dept. of Computer Sci. and Eng. University of Electronic Science and Technology of China Chengdu China State Key Lab. of Integrated Services Networks & School of Telecommun. Eng. Xidian University Xi’an China
Many researches in cognitive science have shown that humans often perform face-voice association for various perception tasks, and some recent data mining works have been designed in emulating such ability intelligent... 详细信息
来源: 评论