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31-40 订阅
排序:
MoAFormer: Aggregating Adjacent Window Features into Local vision Transformer Using Overlapped Attention Mechanism for Volumetric Medical Segmentation  11
MoAFormer: Aggregating Adjacent Window Features into Local V...
收藏 引用
11th International Conference on Computing and pattern recognition, ICCPR 2022
作者: Luo, Yixi Yin, Huayi Du, Xia Department of Computer and Information Engineering Fujian Provincial Key Laboratory of Pattern Recognition and Image Understanding Xiamen University of Technology China
The window-based attention is used to alleviate the problem of abrupt increase in computation as the input image resolution grows and shows excellent performance. However, the problem that aggregating global features ... 详细信息
来源: 评论
SIR-HCL: Semantic-Inconsistency Reasoning and Hybrid Contrastive Learning for Efficient Cross-Emotion Anomaly Detection
收藏 引用
IEEE Transactions on Cognitive and Developmental Systems 2025年
作者: Liu, Xin Chen, Qiyan Cheung, Yiu-Ming Peng, Shu-Juan Huaqiao University Department of Computer Science Xiamen361021 China Hong Kong Baptist University Department of Computer Science SAR Hong Kong Hong Kong Xiamen Key Laboratory of Computer Vision and Pattern Recognition Xiamen361021 China Huaqiao University Fujian Key Laboratory of Big Data Intelligence and Security Xiamen361021 China Huaqiao University Department of Artificial Intelligence Xiamen China Fujian Province University Key Laboratory of Computer Vision and Machine Learning Huaqiao University Xiamen361021 China
Cross-emotion anomaly detection is an emerging and challenging research topic in cognitive analysis field, which aims at identifying the abnormal emotion pair whose semantic patterns are inconsistent across different ... 详细信息
来源: 评论
Interactive Semantic Segmentation With Weak Supervision  22
Interactive Semantic Segmentation With Weak Supervision
收藏 引用
8th International Conference on Computing and Artificial Intelligence, ICCAI 2022
作者: Gong, Lei Wang, Da-Han Wu, Yun Ye, Hai-Li Zhu, Chen-Yan School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China Medical Diagnostic Systems Co. Ltd. Xiamen361000 China
At present, the most advanced semantic segmentation model training mainly relies on pixel-level annotation, that is, annotating the category of each pixel of an image. Such annotation usually is time-consuming and exp... 详细信息
来源: 评论
A Dynamic Spatio-temporal Network with Self-attention for Multi-station Passenger Flow Prediction
A Dynamic Spatio-temporal Network with Self-attention for Mu...
收藏 引用
International Conference on Awareness Science and Technology (iCAST)
作者: Fengzhi Wang Qinzhi Lv Lijuan Liu College of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding
Passenger flow prediction is vitally significant for intelligent transportation systems (ITS). Most of the studies typically focus on the passenger flow prediction for an individual station, and only capture the tempo...
来源: 评论
Pose focus transformer meet inter-part relation
收藏 引用
Expert Systems with Applications 2024年 240卷
作者: Luo, Yanmin Lin, Hongwei Huang, Wenlin Wang, Youjie Du, Jixiang Guo, Jing-Ming College of Computer Science and Technology Huaqiao University Xiamen361021 China Xiamen Key Laboratory of Computer Vision and Pattern Recognition Huaqiao University Xiamen361021 China Maynooth International Engineering College Fuzhou University Fuzhou350108 China Department of Electrical Engineering National Taiwan University of Science and Technology Taipei10607 China
Human pose estimation in crowded scenes is a challenging task. Due to overlap and occlusion, it is difficult to infer pose clues from individual keypoints. We proposed PFFormer, a new transformer-based approach that t... 详细信息
来源: 评论
Collaborative Weighting for Graph Convolutional Networks
Journal of Network Intelligence
收藏 引用
Journal of Network Intelligence 2023年 第2期8卷 432-447页
作者: Chen, Yong Xie, Xiao-Zhu Weng, Wei Zhang, Shan-Dan Li, Tong College of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China College of Computer and Information Engineering Xiamen University of Technology Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China
Graph neural network (GNN), as a powerful method for graph representation, has attracted extensive research interest. Recently, Graph Convolutional Network (GCN) and Graph Attention Network (GAT) have shown superior p... 详细信息
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
NLFA: A Non Local Fusion Alignment Module for Multi-Scale Feature in Object Detection  3rd
NLFA: A Non Local Fusion Alignment Module for Multi-Scale Fe...
收藏 引用
3rd International Symposium on Automation, Mechanical and Design Engineering, SAMDE 2022
作者: Xue, Honghui Ma, Jinshan Cai, Zheyi Fu, Junfang Guo, Feng Weng, Wei Dong, Yunxin Zhang, Zhenchang College of Computer and Information Sciences Fujian Agriculture and Forestry University Fuzhou China Fujian Zhongke Zhongxin Intelligent Technology Co. Ltd Fuzhou China Fujian Newland Auto-ID Tech. Co. Ltd Fuzhou China Department of Computer and Information Engineering Xiamen University of Technology Xiamen China Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen China
Recently, in order to pursue better detection results, more convolutional layers and deeper networks are a direction pursued by everyone. However, more and more down-sampling convolution or up-sampling operations gene... 详细信息
来源: 评论
Handwritten mathematical expression recognition with self-attention  4
Handwritten mathematical expression recognition with self-at...
收藏 引用
4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021
作者: Chi, Xueke Wang, Da-Han Wu, Yuefeng Wu, Yun Fujian Key Laboratory of Pattern Recognition and Image Understanding Xiamen361024 China School of Computer and Information Engineering Xiamen University of Technology Xiamen361024 China
Attention-based encoder-decoder models have made great success on handwritten mathematical expression recognition in recent years. However, this kind of method has the problem of attention drift, because under the loc... 详细信息
来源: 评论
UDC-UNet: Under-Display Camera Image Restoration via U-shape Dynamic Network  17th
UDC-UNet: Under-Display Camera Image Restoration via U-shap...
收藏 引用
17th European Conference on computer vision, ECCV 2022
作者: Liu, Xina Hu, Jinfan Chen, Xiangyu Dong, Chao Shenzhen Key Lab of Computer Vision and Pattern Recognition SIAT-SenseTime Joint Lab Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China University of Macau Zhuhai China Shanghai AI Laboratory Shanghai China
Under-Display Camera (UDC) has been widely exploited to help smartphones realize full-screen displays. However, as the screen could inevitably affect the light propagation process, the images captured by the UDC syste... 详细信息
来源: 评论