Cross-modal vehicle re-identification based on multi-scale features and attention mechanism

Xueqing Jin; Xu Zou; Sheng Zhong

doi:10.1117/12.2691288

7 March 2024 Cross-modal vehicle re-identification based on multi-scale features and attention mechanism

Xueqing Jin, Xu Zou, Sheng Zhong

Proceedings Volume 13086, MIPPR 2023: Pattern Recognition and Computer Vision; 1308609 (2024) https://doi.org/10.1117/12.2691288
Event: Twelfth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2023), 2023, Wuhan, China

Abstract

Our work focuses on exploring the emerging field of cross-modal vehicle re-identification. Achieving accurate cross-modal vehicle re-identification requires a network that can capture local details from two different modality images while effectively fusing their valid information. However, existing methods only consider extracting high-level semantics, leading to a loss of fine-grained details and imprecise identification. Additionally, insufficient attention has been paid to effective information in different modalities, as cross-modality interaction has not been thoroughly explored. To address these issues, we propose a new cross-modal vehicle re-identification network consisting of a multi-scale feature fusion module and a cross-modal attention module. Specifically, the multiscale feature fusion module captures both global high-level semantics and local details by integrating multi-scale information in the feature extraction process, reducing the loss of local details. The cross-modal attention module explores valid information from different modalities and achieves feature-level fusion. We conducted experiments on the RGBNT100 cross-modal vehicle re-identification dataset to verify the proposed method’s effectiveness.

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

Citation Download Citation

Xueqing Jin, Xu Zou, and Sheng Zhong "Cross-modal vehicle re-identification based on multi-scale features and attention mechanism", Proc. SPIE 13086, MIPPR 2023: Pattern Recognition and Computer Vision, 1308609 (7 March 2024); https://doi.org/10.1117/12.2691288

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