TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification

• URL: http://arxiv.org/abs/2111.14290v1
• Date: Mon, 29 Nov 2021 01:27:42 GMT
• Title: TAL: Two-stream Adaptive Learning for Generalizable Person Re-identification
• Authors: Yichao Yan, Junjie Li, Shengcai Liao, Jie Qin, Bingbing Ni, Xiaokang Yang
• Abstract summary: We argue that both domain-specific and domain-invariant features are crucial for improving the generalization ability of re-id models. We name two-stream adaptive learning (TAL) to simultaneously model these two kinds of information. Our framework can be applied to both single-source and multi-source domain generalization tasks.
• Score: 115.31432027711202
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Domain generalizable person re-identification aims to apply a trained model to unseen domains. Prior works either combine the data in all the training domains to capture domain-invariant features, or adopt a mixture of experts to investigate domain-specific information. In this work, we argue that both domain-specific and domain-invariant features are crucial for improving the generalization ability of re-id models. To this end, we design a novel framework, which we name two-stream adaptive learning (TAL), to simultaneously model these two kinds of information. Specifically, a domain-specific stream is proposed to capture training domain statistics with batch normalization (BN) parameters, while an adaptive matching layer is designed to dynamically aggregate domain-level information. In the meantime, we design an adaptive BN layer in the domain-invariant stream, to approximate the statistics of various unseen domains. These two streams work adaptively and collaboratively to learn generalizable re-id features. Our framework can be applied to both single-source and multi-source domain generalization tasks, where experimental results show that our framework notably outperforms the state-of-the-art methods.

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