Abstract. Person re-identification (re-ID) is a highly challenging task
due to large variations of pose, viewpoint, illumination, and occlusion.
Deep metric learning provides a satisfactory solution to person re-ID by
training a deep network under supervision of metric loss, e.g., triplet
loss. However, the performance of deep metric learning is greatly limited
by traditional sampling methods. To solve this problem, we propose a
Hard-Aware Point-to-Set (HAP2S) loss with a soft hard-mining scheme.
Based on the point-to-set triplet loss framework, the HAP2S loss adaptively assigns greater weights to harder samples. Several advantageous
properties are observed when compared with other state-of-the-art loss
functions: 1) Accuracy: HAP2S loss consistently achieves higher re-ID accuracies than other alternatives on three large-scale benchmark datasets;
2) Robustness: HAP2S loss is more robust to outliers than other losses;
3) Flexibility: HAP2S loss does not rely on a specific weight function,
i.e., different instantiations of HAP2S loss are equally effective. 4) Generality: In addition to person re-ID, we apply the proposed method to
generic deep metric learning benchmarks including CUB-200-2011 and
Cars196, and also achieve state-of-the-art results