Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification

Yu-Jhe Li, Fu-En Yang, Yen-Cheng Liu, Yu-Ying Yeh, Xiaofei Du, Yu-Chiang Frank Wang; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018, pp. 172-178

Abstract


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.

Related Material


[pdf] [arXiv]
[bibtex]
@InProceedings{Li_2018_CVPR_Workshops,
author = {Li, Yu-Jhe and Yang, Fu-En and Liu, Yen-Cheng and Yeh, Yu-Ying and Du, Xiaofei and Frank Wang, Yu-Chiang},
title = {Adaptation and Re-Identification Network: An Unsupervised Deep Transfer Learning Approach to Person Re-Identification},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {June},
year = {2018}
}