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Person re-identification (Re-ID) across multiple datasets is a challengingyet important task due to the possibly large distinctions between differentdatasets and the lack of training samples in practical applications. This workproposes a novel unsupervised domain adaption framework which transfersdiscriminative representations from the labeled source domain (dataset) to theunlabeled target domain (dataset). We propose to formulate the domain adaptiontask as an one-class classification problem with a novel domain similarityloss. Given the feature map of any image from a backbone network, a noveldomain adaptive attention model (DAAM) first automatically learns to separatethe feature map of an image to a domain-shared feature (DSH) map and adomain-specific feature (DSP) map simultaneously. Specially, the residualattention mechanism is designed to model DSP feature map for avoiding negativetransfer. Then, a DSH branch and a DSP branch are introduced to learn DSH andDSP feature maps respectively. To reduce domain divergence caused by that thesource and target datasets are collected from different environments, we forceto project the DSH feature maps from different domains to a new nominal domain,and a novel domain similarity loss is proposed based on one-classclassification. In addition, a novel unsupervised person Re-ID loss is proposedto take full use of unlabeled target data. Extensive experiments on theMarket-1501 and DukeMTMC-reID benchmarks demonstrate state-of-the-artperformance of the proposed method. Code will be released to facilitate furtherstudies on the cross-domain person re-identification task.

Domain Adaptive Attention Model for Unsupervised Cross-Domain Person Re-Identification