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A Semantic-guided occlusion simulation based local feature semantic expansion network for person re-identification

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成果类型:
期刊论文
作者:
Zelin Deng;Mingxuan Tang;Ke Nai*;Guiji Li;Shaomiao Chen;...
通讯作者:
Ke Nai
作者机构:
[Zelin Deng; Mingxuan Tang; Ke Nai] School of Computer, Changsha University of Science and Technology, Changsha, 410114, China
[Guiji Li] School of Computer Science and Engineering, Changsha University, Changsha, 410022, China
[Shaomiao Chen] School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, 411201, China
[Pei He] School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, 510000, China
通讯机构:
[Ke Nai] S
School of Computer, Changsha University of Science and Technology, Changsha, 410114, China
语种:
英文
期刊:
Pattern Recognition
ISSN:
0031-3203
年:
2026
卷:
172
页码:
112567
基金类别:
CRediT authorship contribution statement Zelin Deng: Writing – review & editing, Supervision, Resources, Project administration, Investigation, acquisition, Conceptualization. Mingxuan Tang: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Formal analysis, Data curation. Ke Nai: Writing – review & editing, Visualization, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation. Guiji Li: Writing – review & editing, Visualization,
机构署名:
本校为第一且通讯机构
摘要:
Person re-identification is a challenging task which aims to retrieve images of a target pedestrian across different cameras. Currently, existing models generally neglect the semantic relevance of local features, which may cause to limited identification performance. To address this issue, we propose a semantic-guided occlusion simulation based local feature semantic expansion network (FOSENet) to pursue satisfactory performance. Firstly, we propose a semantic-guided occlusion simulation (SGOS) method, which generates semantically relevant occlusion patches and further simulates the occlusion ...

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