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Person re-identification task has been greatly boosted by deep convolutionalneural networks (CNNs) in recent years. The core of which is to enlarge theinter-class distinction as well as reduce the intra-class variance. However, toachieve this, existing deep models prefer to adopt image pairs or triplets toform verification loss, which is inefficient and unstable since the number oftraining pairs or triplets grows rapidly as the number of training data grows.Moreover, their performance is limited since they ignore the fact thatdifferent dimension of embedding may play different importance. In this paper,we propose to employ identification loss with center loss to train a deep modelfor person re-identification. The training process is efficient since it doesnot require image pairs or triplets for training while the inter-classdistinction and intra-class variance are well handled. To boost theperformance, a new feature reweighting (FRW) layer is designed to explicitlyemphasize the importance of each embedding dimension, thus leading to animproved embedding. Experiments on several benchmark datasets have shown thesuperiority of our method over the state-of-the-art alternatives on bothaccuracy and speed.

Deep Person Re-Identification with Improved Embedding and Efficient Training