In this article, we revisit two popular convolutional neural networks in person re-identification (re-ID): verification and identification models. The two models have their respective advantages and limitations due to different loss functions. Here, we shed light on how to combine the two models to learn more discriminative pedestrian descriptors. Specifically, we propose a Siamese network that simultaneously computes the identification loss and verification loss. Given a pair of training images, the network predicts the identities of the two input images and whether they belong to the same identity. Our network learns a discriminative embedding and a similarity measurement at the same time, thus taking full usage of the re-ID annotations. Our method can be easily applied on different pretrained networks. Albeit simple, the learned embedding improves the state-of-the-art performance on two public person re-ID benchmarks. Further, we show that our architecture can also be applied to image retrieval. The code is available at https://github.com/layumi/2016_person_re-ID. 13:2 Z. Zheng et al. Recently, the CNN has shown potential for learning state-of-the-art feature embeddings or deep metrics [5, 18, 38, 43, 44, 47, 56] . As shown in Figure 1 , there are two major types of CNN structures: verification models and identification models. The two models are different concerning input, feature extraction, and loss function for training. Our motivation is to combine the strengths of the two models and learn a more discriminative pedestrian embedding.
Zheng et al. REF propose a new siamese network that simultaneously computes identification loss and verification loss, which learns a discriminative embedding and a similarity measurement at the same time.
A Discriminatively Learned CNN Embedding for Person Reidentification
TOMM
Computer Science