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Adaptive Cross-domain Learning for Generalizable Person Re-identification

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Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13674))

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Abstract

Domain Generalizable Person Re-Identification (DG-ReID) is a more practical ReID task that is trained from multiple source domains and tested on the unseen target domains. Most existing methods are challenged for dealing with the shared and specific characteristics among different domains, which is called the domain conflict problem. To address this problem, we present an Adaptive Cross-domain Learning (ACL) framework equipped with a CrOss-Domain Embedding Block (CODE-Block) to maintain a common feature space for capturing both the domain-invariant and the domain-specific features, while dynamically mining the relations across different domains. Moreover, our model adaptively adjusts the architecture to focus on learning the corresponding features of a single domain at a time without interference from the biased features of other domains. Specifically, the CODE-Block is composed of two complementary branches, a dynamic branch for extracting domain-adaptive features and a static branch for extracting the domain-invariant features. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art performances on the popular benchmarks. Under Protocol-2, our method outperforms previous SOTA by 7.8% and 7.6% in terms of mAP and rank-1 accuracy.

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Acknowledgement

This work is supported in part by Zhejiang Provincial Natural Science Foundation of China under Grant LR19F020004, National Key Research and Development Program of China under Grant 2020AAA0107400, National Natural Science Foundation of China under Grant U20A20222, Key R &D Program of Zhejiang Province, China (2021C01119), the National Natural Science Foundation of China under Grant (62002320, U19B2043).

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Pengyi Zhang, Huanzhang Dou & Xi Li

  2. College of Information Science and Electronic Engineering, Zhejiang University, Hangzhou, China

    Yunlong Yu

  3. Shanghai Institute for Advanced Study, Zhejiang University, Hangzhou, China

    Xi Li

  4. Shanghai AI Laboratory, Hangzhou, China

    Xi Li

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  1. Pengyi Zhang
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  2. Huanzhang Dou
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  3. Yunlong Yu
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  4. Xi Li
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Correspondence to Yunlong Yu or Xi Li .

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Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

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Zhang, P., Dou, H., Yu, Y., Li, X. (2022). Adaptive Cross-domain Learning for Generalizable Person Re-identification. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13674. Springer, Cham. https://doi.org/10.1007/978-3-031-19781-9_13

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