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.Comment: Accepted to ECCV 201