Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification

• URL: http://arxiv.org/abs/2105.12355v1
• Date: Wed, 26 May 2021 06:38:23 GMT
• Title: Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification
• Authors: Shijie Yu, Feng Zhu, Dapeng Chen, Rui Zhao, Haobin Chen, Shixiang Tang, Jinguo Zhu, Yu Qiao
• Abstract summary: We propose a novel training framework, named Multiple Domain Experts Collaborative Learning (MD-ExCo) The MD-ExCo consists of a universal expert and several domain experts. Experiments on DG-ReID benchmarks show that our MD-ExCo outperforms the state-of-the-art methods by a large margin.
• Score: 41.923753462539736
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches suffer from considerable performance degradation when the test target domains exhibit different characteristics from the training ones, known as the domain shift problem. To make ReID more practical and generalizable, we formulate person re-identification as a Domain Generalization (DG) problem and propose a novel training framework, named Multiple Domain Experts Collaborative Learning (MD-ExCo). Specifically, the MD-ExCo consists of a universal expert and several domain experts. Each domain expert focuses on learning from a specific domain, and periodically communicates with other domain experts to regulate its learning strategy in the meta-learning manner to avoid overfitting. Besides, the universal expert gathers knowledge from the domain experts, and also provides supervision to them as feedback. Extensive experiments on DG-ReID benchmarks show that our MD-ExCo outperforms the state-of-the-art methods by a large margin, showing its ability to improve the generalization capability of the ReID models.

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