Learning deep embedding with mini-cluster loss for person re-identification

Recently, the triplet loss is commonly used in many deep person re-identification (ReID) frameworks to learn an embedding space in which similar data points are close and dissimilar data points are far away. However, the triplet loss simply focuses on the relative orders of points. This may lead to a relatively large intra-class variance and then a weak generalization capacity on the test set. In this paper, we propose a mini-cluster loss, which regards images belonging to the same identity as a mini-cluster and treats them as a whole during the training instead of considering them separately. For each mini-cluster in a batch, we define the largest distance between points in a mini-cluster as its inner divergence and the shortest distance with outer points as its outer divergence. By constraining the outer divergence larger than the inner divergence, our framework with the mini-cluster loss achieves the more compact mini-clusters while keeping the diversity distributions of the classes. As a result, a better generalization ability and a higher performance can be obtained. In the extensive experiments, our proposed framework achieves a state-of-the-art performance on two large-scale person ReID datasets (Market1501, DukeMTMC-reID) which clearly demonstrates its effectiveness. Specifically, 72.44% mAP and 87.05% rank-1 score are achieved on the Market1501 dataset with single query setting, 78.17% mAP and 91.05% rank-1 score with multiply query setting, and on the DukeMTMC-reID dataset, 60.19% mAP and 77.20% rank-1 score are obtained.