Person re-identification based on two-layer attention and joint learning

Hong Zhu; Minghai Jiao; Tianshuo Yuan; Wenyan Jiang

doi:10.1117/12.3029702

21 June 2024 Person re-identification based on two-layer attention and joint learning

Hong Zhu, Minghai Jiao, Tianshuo Yuan, Wenyan Jiang

Proceedings Volume 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024); 131673H (2024) https://doi.org/10.1117/12.3029702
Event: International Conference on Remote Sensing, Mapping and Image Processing (RSMIP 2024), 2024, Xiamen, China

Abstract

To solve the problems of intra-class difference and modal difference in cross-modality person re-identification tasks, person re-identification based on two-layer attention and joint learning is proposed. Firstly, the input visible images are enhanced by gray-like channels for joint learning. Then, to obtain more effective feature semantics in space and channels, a two-layer attention module is embedded behind the third convolutional layer of Resnet50. Finally, to promote network convergence and reduce the difference between different modalities, a modality distribution difference loss function is utilized. The effectiveness of this algorithm has been experimentally proved not only in two mainstream public datasets SYSU-MM01 and RegDB, but also in the newly proposed dataset LLCM. The effectiveness of these three modules is proved by ablation experiments on the SYSU-MM01 dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hong Zhu, Minghai Jiao, Tianshuo Yuan, and Wenyan Jiang "Person re-identification based on two-layer attention and joint learning", Proc. SPIE 13167, International Conference on Remote Sensing, Mapping, and Image Processing (RSMIP 2024), 131673H (21 June 2024); https://doi.org/10.1117/12.3029702

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KEYWORDS

RGB color model

Infrared imaging

Feature extraction

Image enhancement

Image processing

Visible radiation

Deep learning

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