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.
@article{arxiv.1804.09347,
title = {Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification},
author = {Yu-Jhe Li and Fu-En Yang and Yen-Cheng Liu and Yu-Ying Yeh and Xiaofei Du and Yu-Chiang Frank Wang},
journal= {arXiv preprint arXiv:1804.09347},
year = {2018}
}