中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 自动化研究所
  3. 中国科学院自动化研究所
  4. 智能感知与计算研究中心
A Two-Stream Hybrid Convolution-Transformer Network Architecture for Clothing-Change Person Re-Identification

文献类型：期刊论文

作者Wu, Junyi1; Huang, Yan2; Gao, Min3; Gao, Zhipeng1; Zhao, Jianqiang1; Zhang, Huiji1; Zhang, Anguo4,5,6,7
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2024
卷号26页码:5326-5339
关键词Clothing-change person re-identification ID-unique feature feature supplement module hierarchical supervision
ISSN号1520-9210
DOI10.1109/TMM.2023.3331569
通讯作者Huang, Yan(yan.huang@cripac.ia.ac.cn) ; Zhang, Anguo(anguo.zhang@hotmail.com)
英文摘要Long-term (also called Clothing-Change) person re-identification (CC-reID) aims at confirming the identity of pedestrians captured at diverse locations and/or times. Current CC-reID methods heavily rely on ID features learned by the CNN architecture. However, with limited receptive fields, CNN is hard to effectively explore some unique but discriminative ID features (e.g., hair style, tattoo and accessories) from small body regions. Compared with CNN, Transformer has certain merits in exploring more diverse ID-unique features and retaining more details by the multi-head self-attention design and the removal of down-sampling operation. In this paper, a two-stream hybrid Convolution-Transformer Network (CT-Net) is proposed for CC-reID by combining both CNN and Transformer parallelly in an end-to-end learning scheme. Specifically, CT-Net contains a CNN-based stream (C-Stream) and a Transformer-based stream (T-Stream). Compared with using C-Stream only, T-Stream is used to encourage the C-Stream to explore more detailed ID-unique features when the clothing information is no reliable in CC-reID. Specifically, a Feature Supplement Module (FSM) is proposed to transfer features learned by T-Stream to C-Stream from low-level to high-level for mining more ID-unique feature. In order to further enhance the discriminability and complementary of ID features learned by our CT-Net, we also introduce a hierarchical supervision with bilinear pooling (HSBP). Experimental results demonstrate that CT-Net performs favorably against the state-of-the-art methods over three CC-reID benchmarks. Meanwhile, CT-Net also demonstrates good generalization ability by achieving comparable performance on traditional person re-ID datasets such as Market-1501 and DukeMTMC-reID.
WOS关键词RECOGNITION ; ATTENTION
资助项目National Natural Science Foundation of China
WOS研究方向Computer Science ; Telecommunications
语种英语
WOS记录号WOS:001189435600030
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
资助机构National Natural Science Foundation of China
源URL[http://ir.ia.ac.cn/handle/173211/58120]  
专题自动化研究所_智能感知与计算研究中心
通讯作者Huang, Yan; Zhang, Anguo
作者单位1.Xiamen Meiya Pico Informat Co Ltd, Xiamen Meiya Informat Secur Res Inst Co Ltd, AI Res Ctr, Xiamen 361008, Peoples R China
2.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Natl Lab Pattern Recognit, Beijing 100045, Peoples R China
3.Fuzhou Univ, Coll Phys & Informat Engn, Fujian Key Lab Intelligent Proc & Wireless Transmi, Fuzhou 350025, Peoples R China
4.Anhui Univ, Sch Artificial Intelligence, Hefei 230039, Peoples R China
5.Minist Educ, Res Ctr Autonomous Unmanned Syst Technol, Hefei 230039, Peoples R China
6.Anhui Prov Engn Res Ctr Unmanned Syst & Intelligen, Hefei 230039, Peoples R China
7.Univ Macau, Inst Microelect, Taipa 999078, Macao, Peoples R China
推荐引用方式
GB/T 7714		 Wu, Junyi,Huang, Yan,Gao, Min,et al. A Two-Stream Hybrid Convolution-Transformer Network Architecture for Clothing-Change Person Re-Identification[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2024,26:5326-5339.
APA Wu, Junyi.,Huang, Yan.,Gao, Min.,Gao, Zhipeng.,Zhao, Jianqiang.,...&Zhang, Anguo.(2024).A Two-Stream Hybrid Convolution-Transformer Network Architecture for Clothing-Change Person Re-Identification.IEEE TRANSACTIONS ON MULTIMEDIA,26,5326-5339.
MLA Wu, Junyi,et al."A Two-Stream Hybrid Convolution-Transformer Network Architecture for Clothing-Change Person Re-Identification".IEEE TRANSACTIONS ON MULTIMEDIA 26(2024):5326-5339.

入库方式： OAI收割

来源：自动化研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。