Person re-identification (Re-ID) aims at recognizing the same person from
images taken across different cameras. To address this task, one typically
requires a large amount labeled data for training an effective Re-ID model,
which might not be practical for real-world applications. To alleviate this
limitation, we choose to exploit a sufficient amount of pre-existing labeled
data from a different (auxiliary) dataset. By jointly considering such an
auxiliary dataset and the dataset of interest (but without label information),
our proposed adaptation and re-identification network (ARN) performs
unsupervised domain adaptation, which leverages information across datasets and
derives domain-invariant features for Re-ID purposes. In our experiments, we
verify that our network performs favorably against state-of-the-art
unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID
methods which require fully supervised data for training.

该论文提出了一种基于无监督领域自适应的 person re-identification（Re-ID）网络模型，能够在没有外部监督数据的情况下，利用来自多个数据源的信息，提取出具有领域不变性的特征向量，并在实验中取得了比其他同类方法更好的效果。