This work considers the problem of domain shift in person re-identification.Being trained on one dataset, a re-identification model usually performs much worse on unseen data. Partially this gap is caused by the relatively small scale of person re-identification datasets (compared to face recognition ones, for instance), but it is also related to training objectives. We propose to use the metric learning objective, namely AM-Softmax loss, and some additional training practices to build well-generalizing, yet, computationally efficient models. We use recently proposed Omni-Scale Network (OSNet) architecture combined with several training tricks and architecture adjustments to obtain state-of-the art results in cross-domain generalization problem on a large-scale MSMT17 dataset in three setups: MSMT17-all->DukeMTMC, MSMT17-train->Market1501 and MSMT17-all->Market1501.
@article{arxiv.2003.07618,
title = {Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning},
author = {Vladislav Sovrasov and Dmitry Sidnev},
journal= {arXiv preprint arXiv:2003.07618},
year = {2021}
}
Comments
Submitted to International Conference on Pattern Recognition (ICPR 2020)