Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling

• URL: http://arxiv.org/abs/2009.09445v1
• Date: Sun, 20 Sep 2020 14:54:42 GMT
• Title: Unsupervised Domain Adaptation for Person Re-Identification through Source-Guided Pseudo-Labeling
• Authors: Fabian Dubourvieux, Romaric Audigier, Angelique Loesch, Samia Ainouz, Stephane Canu
• Abstract summary: Person Re-Identification (re-ID) aims at retrieving images of the same person taken by different cameras. Unsupervised Domain Adaptation (UDA) is an interesting research direction for this challenge as it avoids a costly annotation of the target data. We introduce a framework which relies on a two-branch architecture optimizing classification and triplet loss based metric learning in source and target domains.
• Score: 2.449909275410288
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Person Re-Identification (re-ID) aims at retrieving images of the same person taken by different cameras. A challenge for re-ID is the performance preservation when a model is used on data of interest (target data) which belong to a different domain from the training data domain (source data). Unsupervised Domain Adaptation (UDA) is an interesting research direction for this challenge as it avoids a costly annotation of the target data. Pseudo-labeling methods achieve the best results in UDA-based re-ID. Surprisingly, labeled source data are discarded after this initialization step. However, we believe that pseudo-labeling could further leverage the labeled source data in order to improve the post-initialization training steps. In order to improve robustness against erroneous pseudo-labels, we advocate the exploitation of both labeled source data and pseudo-labeled target data during all training iterations. To support our guideline, we introduce a framework which relies on a two-branch architecture optimizing classification and triplet loss based metric learning in source and target domains, respectively, in order to allow \emph{adaptability to the target domain} while ensuring \emph{robustness to noisy pseudo-labels}. Indeed, shared low and mid-level parameters benefit from the source classification and triplet loss signal while high-level parameters of the target branch learn domain-specific features. Our method is simple enough to be easily combined with existing pseudo-labeling UDA approaches. We show experimentally that it is efficient and improves performance when the base method has no mechanism to deal with pseudo-label noise or for hard adaptation tasks. Our approach reaches state-of-the-art performance when evaluated on commonly used datasets, Market-1501 and DukeMTMC-reID, and outperforms the state of the art when targeting the bigger and more challenging dataset MSMT.

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