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Information disentanglement based cross-modal representation learning for visible-infrared person re-identification

  • 1227: Content-based Image Retrieval
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Abstract

Visible-infrared person re-identification (VI-ReID) is an important but very challenging task in the automated video surveillance and forensics. Although existing VI-ReID methods have achieved very encouraging results, how to make full use of the useful information contained in cross-modality visible and infrared images has not been well studied. In this paper, we propose an Information Disentanglement based Cross-modal Representation Learning (IDCRL) approach for VI-ReID. Specifically, IDCRL first extracts the shared and specific features from data of each modality by using the shared feature learning module and the specific feature learning module, respectively. To ensure that the shared and specific information can be well disentangled, we impose an orthogonality constraint on the shared and specific features of each modality. To make the shared features extracted from the visible and infrared images of the same person own high similarity, IDCRL designs a shared feature consistency constraint. Furthermore, IDCRL uses a modality-aware loss to ensure that the useful modality-specific features can be extracted from each modality effectively. Then, the obtained shared and specific features are concatenated as the representation of each image. Finally, identity loss function and cross-modal discriminant loss function are employed to enhance the discriminability of the obtained image representation. We conducted comprehensive experiments on the benchmark visible-infrared pedestrian datasets (SYSU-MM01 and RegDB) to evaluate the efficacy of our IDCRL approach. Experimental results demonstrate that IDCRL outperforms the compared state-of-the-art methods. On the SYSU-MM01 dataset, the rank-1 matching rate of our approach reaches 62.35% and 71.64% in the all-search and in-door modes, respectively. On the RegDB dataset, the rank-1 result of our approach reaches 76.32% and 75.49% in the visible to thermal and thermal to visible modes, respectively.

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Data Availability

The SYSU-MM01 dataset that supports the findings of this study is available from the corresponding author upon reasonable request. The researchers will need to sign a dataset release agreement before they can obtain the download link. The related page for SYSU-MM01 is https://github.com/wuancong/SYSU-MM01. The RegDB dataset that supports the findings of this study is publicly available online at https://github.com/bismex/HiCMD and https://drive.google.com/file/d/1gnVt9GIQSvium_mcxc7AWLhSXm6lNWsa/view?usp=sharing.

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Acknowledgments

This work was supported by the NSFC Project (No. 62176069), Young Scientists Fund of the National Natural Science Foundation of China (No. 62006070), Natural Science Foundation of Henan Province (Nos. 202300410092 and 202300410093), Key Scientific and Technological Project of Henan Province of China (Nos. 222102210204 and 222102210197), and the Excellent Youth Scientific Research Project of Hunan Education Department (No. 21B0582).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Henan University, Kaifeng, China

    Xiaoke Zhu, Minghao Zheng, Caihong Yuan & Fan Zhang

  2. Henan Key Laboratory of Big Data Analysis and Processing, Henan University, Kaifeng, China

    Xiaopan Chen

  3. School of Computer, Wuhan University, Wuhan, China

    Xinyu Zhang

  4. Henan Engineering Research Center of Intelligent Technology and Application, Henan University, Kaifeng, China

    Fan Zhang

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  1. Xiaoke Zhu
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Correspondence to Xiaopan Chen or Xinyu Zhang.

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Zhu, X., Zheng, M., Chen, X. et al. Information disentanglement based cross-modal representation learning for visible-infrared person re-identification. Multimed Tools Appl 82, 37983–38009 (2023). https://doi.org/10.1007/s11042-022-13669-3

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