Deep Person Re-Identification with Improved Embedding and Efficient Training
This work addresses a specific bottleneck in person re-identification for computer vision applications, offering incremental improvements in training efficiency and performance.
The paper tackles the inefficiency and instability of using image pairs or triplets in deep person re-identification by proposing a method that employs identification loss with center loss for efficient training and a feature reweighting layer to improve embedding, resulting in superior accuracy and speed on benchmark datasets.
Person re-identification task has been greatly boosted by deep convolutional neural networks (CNNs) in recent years. The core of which is to enlarge the inter-class distinction as well as reduce the intra-class variance. However, to achieve this, existing deep models prefer to adopt image pairs or triplets to form verification loss, which is inefficient and unstable since the number of training pairs or triplets grows rapidly as the number of training data grows. Moreover, their performance is limited since they ignore the fact that different dimension of embedding may play different importance. In this paper, we propose to employ identification loss with center loss to train a deep model for person re-identification. The training process is efficient since it does not require image pairs or triplets for training while the inter-class distinction and intra-class variance are well handled. To boost the performance, a new feature reweighting (FRW) layer is designed to explicitly emphasize the importance of each embedding dimension, thus leading to an improved embedding. Experiments on several benchmark datasets have shown the superiority of our method over the state-of-the-art alternatives on both accuracy and speed.