Building Computationally Efficient and Well-Generalizing Person Re-Identification Models with Metric Learning
This work addresses domain shift for person re-identification models, offering incremental improvements in computational efficiency and generalization.
The paper tackles domain shift in person re-identification by using AM-Softmax loss and training practices with OSNet architecture, achieving state-of-the-art cross-domain generalization results on MSMT17 dataset in three setups.
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.