Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification
Cheng Wang, Qian Zhang, Chang Huang, Wenyu Liu, Xinggang Wang
Abstract
We propose a novel deep network called Mancs that solves the person re-identification problem from the following aspects: fully utilizing the attention mechanism for the person misalignment problem and properly sampling for the ranking loss to obtain more stable person representation. Technically, we contribute a novel fully attentional block which is deeply supervised and can be plugged into any CNN, and a novel curriculum sampling method which is effective for training ranking losses. The learning tasks are integrated into a unified framework and jointly optimized. Experiments have been carried out on Market1501, CUHK03 and DukeMTMC. All the results show that Mancs can significantly outperform the previous state-of-the-arts. In addition, the effectiveness of the newly proposed ideas has been confirmed by extensive ablation studies.
BibTeX
@inproceedings{eccv2018_mancsamultitaska,
title = {Mancs: A Multi-task Attentional Network with Curriculum Sampling for Person Re-identification},
author = {Cheng Wang and Qian Zhang and Chang Huang and Wenyu Liu and Xinggang Wang},
booktitle = {ECCV 2018},
year = {2018}
}