MVP Matching: A Maximum-Value Perfect Matching for Mining Hard Samples, With Application to Person Re-Identification

Han Sun, Zhiyuan Chen, Shiyang Yan, Lin Xu; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 6737-6747

Abstract


How to correctly stress hard samples in metric learning is critical for visual recognition tasks, especially in challenging person re-ID applications. Pedestrians across cameras with significant appearance variations are easily confused, which could bias the learned metric and slow down the convergence rate. In this paper, we propose a novel weighted complete bipartite graph based maximum-value perfect (MVP) matching for mining the hard samples from a batch of samples. It can emphasize the hard positive and negative sample pairs respectively, and thus relieve adverse optimization and sample imbalance problems. We then develop a new batch-wise MVP matching based loss objective and combine it in an end-to-end deep metric learning manner. It leads to significant improvements in both convergence rate and recognition performance. Extensive empirical results on five person re-ID benchmark datasets, i.e., Market-1501, CUHK03-Detected, CUHK03-Labeled, Duke-MTMC, and MSMT17, demonstrate the superiority of the proposed method. It can accelerate the convergence rate significantly while achieving state-of-the-art performance. The source code of our method is available at https://github.com/IAAI-CVResearchGroup/MVP-metric.

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[bibtex]
@InProceedings{Sun_2019_ICCV,
author = {Sun, Han and Chen, Zhiyuan and Yan, Shiyang and Xu, Lin},
title = {MVP Matching: A Maximum-Value Perfect Matching for Mining Hard Samples, With Application to Person Re-Identification},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}