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Leaning compact and representative features for cross-modality person re-identification

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Abstract

This paper pays close attention to the cross-modality visible-infrared person re-identification (VI Re-ID) task, which aims to match pedestrian samples between visible and infrared modes. In order to reduce the modality-discrepancy between samples from different cameras, most existing works usually use constraints based on Euclidean metric. Because of the Euclidean based distance metric strategy cannot effectively measure the internal angles between the embedded vectors, the existing solutions cannot learn the angularly discriminative feature embedding. Since the most important factor affecting the classification task based on embedding vector is whether there is an angularly discriminative feature space, in this paper, we present a new loss function called Enumerate Angular Triplet (EAT) loss. Also, motivated by the knowledge distillation, to narrow down the features between different modalities before feature embedding, we further present a novel Cross-Modality Knowledge Distillation (CMKD) loss. Benefit from the above two considerations, the embedded features are discriminative enough in a way to tackle modality-discrepancy problem. The experimental results on RegDB and SYSU-MM01 datasets have demonstrated that the proposed method is superior to the other most advanced methods in terms of impressive performance. Code is available at https://github.com/IVIPLab/LCCRF.

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Acknowledgements

This work was supported in part by the National Key Research and Development Program of China under Project nos. 2018AAA0100102 and 2018AAA0100100, the National Natural Science Foundation of China under Grant nos. 61972212, 61772568 and 61833011, the Natural Science Foundation of Jiangsu Province under Grant no. BK20190089, the Six Talent Peaks Project in Jiangsu Province under Grant no. RJFW-011, Youth science and technology innovation talent of Guangdong Special Support Program, and Open Fund Project of Provincial Key Laboratory for Computer Information Processing Technology (Soochow University) (No. KJS1840).

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Authors and Affiliations

  1. Institute of Advanced Technology, Nanjing University of Posts and Telecommunications, Nanjing, China

    Guangwei Gao, Hao Shao & Fei Wu

  2. Digital Content and Media Sciences Research Division, National Institute of Informatics, Tokyo, Japan

    Guangwei Gao & Yi Yu

  3. Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, Sun Yat-sen University, Guangzhou, China

    Meng Yang

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  1. Guangwei Gao
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Guangwei Gao and Hao Shao contributed equally to this work.

This article belongs to the Topical Collection: Special Issue on Synthetic Media on the Web

Guest Editors: Huimin Lu, Xing Xu, Jože Guna, and Gautam Srivastava

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Gao, G., Shao, H., Wu, F. et al. Leaning compact and representative features for cross-modality person re-identification. World Wide Web 25, 1649–1666 (2022). https://doi.org/10.1007/s11280-022-01014-5

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