Style Normalization and Restitution for Generalizable Person Re-identification

• URL: http://arxiv.org/abs/2005.11037v1
• Date: Fri, 22 May 2020 07:15:10 GMT
• Title: Style Normalization and Restitution for Generalizable Person Re-identification
• Authors: Xin Jin, Cuiling Lan, Wenjun Zeng, Zhibo Chen, Li Zhang
• Abstract summary: We design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains. We propose a simple yet effective Style Normalization and Restitution (SNR) module. Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks.
• Score: 89.482638433932
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing fully-supervised person re-identification (ReID) methods usually suffer from poor generalization capability caused by domain gaps. The key to solving this problem lies in filtering out identity-irrelevant interference and learning domain-invariant person representations. In this paper, we aim to design a generalizable person ReID framework which trains a model on source domains yet is able to generalize/perform well on target domains. To achieve this goal, we propose a simple yet effective Style Normalization and Restitution (SNR) module. Specifically, we filter out style variations (e.g., illumination, color contrast) by Instance Normalization (IN). However, such a process inevitably removes discriminative information. We propose to distill identity-relevant feature from the removed information and restitute it to the network to ensure high discrimination. For better disentanglement, we enforce a dual causal loss constraint in SNR to encourage the separation of identity-relevant features and identity-irrelevant features. Extensive experiments demonstrate the strong generalization capability of our framework. Our models empowered by the SNR modules significantly outperform the state-of-the-art domain generalization approaches on multiple widely-used person ReID benchmarks, and also show superiority on unsupervised domain adaptation.

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