Discriminative Feature Representation for Person Re-identification by Batch-contrastive Loss
Proceedings of The 10th Asian Conference on Machine Learning, PMLR 95:208-219, 2018.
In the past few years, person re-identification (reID) has developed rapidly due to the success of deep convolutional neural networks. The softmax loss function is an important component for learning discriminative features. However, the classifier trained by the softmax loss is difficult to distinguish the hard samples. In this work, we introduce a new auxiliary loss function, called batch-contrastive loss, for person reID to further separate the features of different identities and pulls the features of same identity closer. Furthermore, the proposed loss function does not rely on the pairwise or triplet sampling which is commonly used in the Siamese model. We test our loss function on two large-scale person reID benchmarks, Market-1501 and DukeMTMC datasets. Under the combination of the batch-contrastive loss and the softmax loss, even only employing the generic L2-distance metric, we can achieve competitive results among the state-of-the-arts.