Work

Attribute-Guided Alignment Model for Person Re-Identification With Feature Distillation and Enhancement

Year: 2025

Type: article

Abstract: This paper proposes an attribute-guided alignment model for person re-identification with feature distillation and enhancement. The proposed attribute-guided part-level distillation model learns spatial body-part information, enabling the alignment of attribute features with their corresponding body regions. Hence, the localization ability of the attention mechanism in spatial information is fully exploited to address the feature misalignment issue in person re-identification tasks. Furthermore, an attribute-guided part-level feature enhancement method is proposed, which re-weights local features by selecting the top-3 most distinctive features for person re-identification during the inference phase. Experimental results on person re-identification datasets demonstrate the effectiveness of the proposed model.

Source: IEEE Access

DOI: 10.1109/access.2025.3561201

Authors: Rongnan Huang, Simeng Peng, Yaotao Xiahou, Xiaochao Li

Institution: Xiamen University

Language: English

Topic: Video Surveillance and Tracking Methods

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075

Funders: National Key Laboratory of Aerodynamic Design and Research, National Science and Technology Major Project