Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning

• URL: http://arxiv.org/abs/2010.09561v1
• Date: Mon, 19 Oct 2020 14:42:29 GMT
• Title: Domain Generalized Person Re-Identification via Cross-Domain Episodic Learning
• Authors: Ci-Siang Lin, Yuan-Chia Cheng, Yu-Chiang Frank Wang
• Abstract summary: We present an episodic learning scheme which advances meta learning strategies to exploit the observed source-domain labeled data. Our experiments on four benchmark datasets confirm the superiority of our method over the state-of-the-arts.
• Score: 31.17248105464821
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Aiming at recognizing images of the same person across distinct camera views, person re-identification (re-ID) has been among active research topics in computer vision. Most existing re-ID works require collection of a large amount of labeled image data from the scenes of interest. When the data to be recognized are different from the source-domain training ones, a number of domain adaptation approaches have been proposed. Nevertheless, one still needs to collect labeled or unlabelled target-domain data during training. In this paper, we tackle an even more challenging and practical setting, domain generalized (DG) person re-ID. That is, while a number of labeled source-domain datasets are available, we do not have access to any target-domain training data. In order to learn domain-invariant features without knowing the target domain of interest, we present an episodic learning scheme which advances meta learning strategies to exploit the observed source-domain labeled data. The learned features would exhibit sufficient domain-invariant properties while not overfitting the source-domain data or ID labels. Our experiments on four benchmark datasets confirm the superiority of our method over the state-of-the-arts.

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