Research on Cross-modal Pedestrian Re-identification Based on Transformer
Year: 2025
Type: article
Abstract: Visible infrared human body recognition (VI ReID) is a challenging task for complex modal change retrieval. Existing methods usually focus on extracting discriminative visual features, while ignoring the reliability and commonness of visual features between different modes. In this paper, we propose a new deep learning framework, called multi-scale local progressive transformers (MLT), for effective VI-ReID. In order to reduce the negative impact of modal gap, we first take the gray image as an auxiliary mode, take the Transformer model as the benchmark, and propose a progressive learning strategy. The sea attention mechanism is fused with the dilateformer to further improve the discrimination ability of reliable features, and its feasibility is increased through ablation experiments.
DOI: 10.54097/8drb0c69
Authors: Zheyu Fu, Xiaoguang Hu, Xu Wang
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4408964124