A Person Re-Identification Baseline Based on Attention Block Neural Architecture Search

Person re-identification (re-ID) aims to match images of the same person in different camera views. The general convolutional neural network has the problem of insufficient ability to discriminate specific targets, resulting in limited feature representation when directly applied in person re-ID task. In this paper, a person re-ID baseline model based on attention block neural architecture search is proposed. Since the attention mechanism is helpful to improve the feature expression ability of the network, attention blocks are automatically searched and added to ResNet-50 model, which aims to find the most suitable model structure for person re-ID task. Besides, in order to integrate information between the samples of same identity, an intra-class self-distillation loss is introduced according to the idea of knowledge integration. Experiments on two popular datasets confirm the effectiveness of our baseline. The code has been released in https://github.com/Nicholasxin/Attention-NASReID.