CASIA OpenIR  > 紫东太初大模型研究中心
Explicit Attention Modeling for Pedestrian Attribute Recognition
Jinyi Fang; Bingke Zhu; Yingying Chen; Jinqiao Wang; Ming Tang
2023
Conference NameIEEE International Conference on Multimedia and Expo (ICME)
Conference Date2023.7.10-2023.7.14
Conference PlaceBrisbane, Australia
Abstract

Recent studies on pedestrian attribute recognition have achieved significant improvements by utilizing complex networks and attention mechanisms. However, most of these studies learn the attention map implicitly through the class activation map. In this paper, we propose an explicit attention modeling approach for pedestrian attribute recognition. We construct a mask branch to learn the attention maps with a lightweight feature pyramid network. The features inside the specific mask are then averaged to obtain the scores for attribute recognition. Additionally, we introduce spatial and semantic distillation to improve the consistency of attention masks and attribute scores. Our experiments demonstrate that the proposed explicit attention modeling can achieve state-of-the-art performance on PA100K, PETA, and PAR datasets with negligible parameters.

Indexed ByEI
Language英语
Sub direction classification图像视频处理与分析
planning direction of the national heavy laboratory视觉信息处理
Paper associated data
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57372
Collection紫东太初大模型研究中心
Corresponding AuthorJinyi Fang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Jinyi Fang,Bingke Zhu,Yingying Chen,et al. Explicit Attention Modeling for Pedestrian Attribute Recognition[C],2023.
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