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IAUnet: Global Context-Aware Feature Learning for Person Reidentification

文献类型：期刊论文

作者Hou, Ruibing1,2; Ma, Bingpeng2; Chang, Hong1,2; Gu, Xinqian1,2; Shan, Shiguang1,2,3; Chen, Xilin1,2
刊名IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
出版日期2021-10-01
卷号32期号:10页码:4460-4474
关键词Context modeling Feature extraction Computational modeling Semantics Aggregates Visualization Task analysis Feature enhancing interaction-aggregation person reidentification (reID) spatial-temporal context modeling
ISSN号2162-237X
DOI10.1109/TNNLS.2020.3017939
英文摘要Person reidentification (reID) by convolutional neural network (CNN)-based networks has achieved favorable performance in recent years. However, most of existing CNN-based methods do not take full advantage of spatial-temporal context modeling. In fact, the global spatial-temporal context can greatly clarify local distractions to enhance the target feature representation. To comprehensively leverage the spatial-temporal context information, in this work, we present a novel block, interaction-aggregation-update (IAU), for high-performance person reID. First, the spatial-temporal IAU (STIAU) module is introduced. STIAU jointly incorporates two types of contextual interactions into a CNN framework for target feature learning. Here, the spatial interactions learn to compute the contextual dependencies between different body parts of a single frame, while the temporal interactions are used to capture the contextual dependencies between the same body parts across all frames. Furthermore, a channel IAU (CIAU) module is designed to model the semantic contextual interactions between channel features to enhance the feature representation, especially for small-scale visual cues and body parts. Therefore, the IAU block enables the feature to incorporate the globally spatial, temporal, and channel context. It is lightweight, end-to-end trainable, and can be easily plugged into existing CNNs to form IAUnet. The experiments show that IAUnet performs favorably against state of the art on both image and video reID tasks and achieves compelling results on a general object categorization task. The source code is available at https://github.com/blue-blue272/ImgReID-IAnet.
资助项目Natural Science Foundation of China (NSFC)[61732004] ; Natural Science Foundation of China (NSFC)[61876171] ; Natural Science Foundation of China (NSFC)[61976203] ; Fundamental Research Funds for the Central Universities
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000704111000018
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/17019]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Ma, Bingpeng
作者单位1.Chinese Acad Sci, Key Lab Intelligent Informat Proc, Inst Comp Technol, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 100049, Peoples R China
3.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China
推荐引用方式
GB/T 7714		 Hou, Ruibing,Ma, Bingpeng,Chang, Hong,et al. IAUnet: Global Context-Aware Feature Learning for Person Reidentification[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2021,32(10):4460-4474.
APA Hou, Ruibing,Ma, Bingpeng,Chang, Hong,Gu, Xinqian,Shan, Shiguang,&Chen, Xilin.(2021).IAUnet: Global Context-Aware Feature Learning for Person Reidentification.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,32(10),4460-4474.
MLA Hou, Ruibing,et al."IAUnet: Global Context-Aware Feature Learning for Person Reidentification".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 32.10(2021):4460-4474.

入库方式： OAI收割

来源：计算技术研究所

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