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Link to original content: https://unpaywall.org/10.1007/S11042-023-16260-6
Dual-attentive cascade clustering learning for visible-infrared person re-identification | Multimedia Tools and Applications Skip to main content
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Dual-attentive cascade clustering learning for visible-infrared person re-identification

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Abstract

Visible-infrared person re-identification (VI Re-ID) is challenging work due to huge inter-modality discrepancies and high similarity among inter-identity infrared images. Current methods aim to alleviate the modality discrepancies by using attention mechanisms and identity learning. However, most of these methods are too complex or fine-grained, which can instead destroy the integrity of subtle information and unavoidably diminishes the distinctiveness of features. Different from existing methods, we propose a novel Dual-Attentive Cascade Clustering Learning Network (DA\(C^2\)LNet) to alleviate inter-modality differences and reduce inter-identity similarities. DA\(C^2\)LNet focuses on learning key and useful information by discovering subtle information distributed in each part of the person’s body, which includes the channel attention module (CAM) and part-based attention module (PbAM). Specifically, we first apply CAM to alleviate modality discrepancies and enhance feature discrimination. Then, we design a PbAM, which is different from spatial attention in pixels, it generates several part pattern maps corresponding to different parts of the person’s body to mine overall nuances for minimizing inter-identity similarities. The two modules are cascaded together to learn distinguishing features. Finally, we introduce a center cluster learning manner to reduce intra-identity inter-modality discrepancies and increase inter-identity variances. Extensive experimental results on two public datasets (SYSU-MM01 and RegDB) demonstrate that DA\(C^2\)LNet outperforms state-of-the-art methods.

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Acknowledgements

This work was supported by Anhui Science and Technology Department Project (Grant No. 202004a05020030),Anhui Photovoltaic Industry Generic Technology Research Center (Granted No. 2022AHPV000001) ,and Natural Science Foundation Project of Anhui Province(Granted No. 2022AH040200).

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Authors and Affiliations

  1. School of Physics and Electronic Engineering, Fuyang Normal University, Qinghe, Fuyang, 236037, Anhui, China

    Xianju Wang, Yong Zhu & Shuguang Chen

  2. Graduate School, Angeles University Foundation, Angeles, 2009, Philippines

    Xianju Wang

  3. School of Computer Science and Information Engineering, Hefei University of Technology, Tunxi, Hefei, 230009, Anhui, China

    Cuiqun Chen

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  1. Xianju Wang
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Chen Cuiqun, Zhu Yong, and Chen Shuguang contributed equally to this work.

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Wang, X., Chen, C., Zhu, Y. et al. Dual-attentive cascade clustering learning for visible-infrared person re-identification. Multimed Tools Appl 83, 19729–19746 (2024). https://doi.org/10.1007/s11042-023-16260-6

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