Person re-identification (Re-ID) across multiple datasets is a challenging
yet important task due to the possibly large distinctions between different
datasets and the lack of training samples in practical applications. This work
proposes a novel unsupervised domain adaption framework which transfers
discriminative representations from the labeled source domain (dataset) to the
unlabeled target domain (dataset). We propose to formulate the domain adaption
task as an one-class classification problem with a novel domain similarity
loss. Given the feature map of any image from a backbone network, a novel
domain adaptive attention model (DAAM) first automatically learns to separate
the feature map of an image to a domain-shared feature (DSH) map and a
domain-specific feature (DSP) map simultaneously. Specially, the residual
attention mechanism is designed to model DSP feature map for avoiding negative
transfer. Then, a DSH branch and a DSP branch are introduced to learn DSH and
DSP feature maps respectively. To reduce domain divergence caused by that the
source and target datasets are collected from different environments, we force
to project the DSH feature maps from different domains to a new nominal domain,
and a novel domain similarity loss is proposed based on one-class
classification. In addition, a novel unsupervised person Re-ID loss is proposed
to take full use of unlabeled target data. Extensive experiments on the
Market-1501 and DukeMTMC-reID benchmarks demonstrate state-of-the-art
performance of the proposed method. Code will be released to facilitate further
studies on the cross-domain person re-identification task.

该研究提出了一种新颖的无监督域自适应框架，用于将分类学表示从标记源域（数据集）转换到未标记的目标域（数据集），并使用一种新颖的域相似性损失将域自适应任务制定为一个单类分类问题。