# Attention-Aware Adversarial Network for Person Re-Identification
## 1 Introduction  
Person re-identification (re-ID) is a fundamental problem in computer vision, which aims to re-identify a person of interest in other cameras. In recent years, person re-ID has achieved increasing attention due to its wide applications and great potentials, such as criminal investigation and security enhancement. Although recent deep learning-based person re-ID networks achieve favorable performance, most of them still suffer from a lack of training samples. Training images in most datasets are collected from manually-cropped person images. Therefore, it is time consuming to create a large-scale database. To tackle this problem, the methods of [1; 2; 3; 4] propose to learn robust and discriminative features from limited training samples in a deep neural network. As implemented by most methods, common data augmentation methods, e.g., randomly cropping, flipping, and color jittering, are not sufficient to achieve high-quality results. One approach to augment training samples is to generate multiple images based on each occluded image by random erasure [5; 6]. Although the performance is improved by this method, it aggravates the memory and storage requirements. The method [7] enriches the data on the feature map level. As a result, the learned object detector is robust to different conditions,i.e., occlusion, deformation, illumination, etc. Inspired by [7], we perform data augmentation on the level of feature maps by erasing (occluding) different spatial regions of the feature maps. Existing person re-ID methods face various intrinsic difficulties, such as occlusion, illumination, image resolution, and noisy background, to name a few. Many methods [8; 9; 10; 11] address these problems by representing the input person image with discriminative feature maps and matching them in a task-specific metric space. However, the performance of most of these methods may fall short due to limited training samples, e.g., training images are not sufficient to cover the complex conditions of real scenes. As a result, training models easily over-fit the training datasets. The recently-proposed image generation models, the generative adversarial networks (GANs) [8], provide powerful tools to handle this problem by generating more realistic style images. Many methods benefit from the high-performance GAN model [12; 13]. The work in [5; 6] generated adversarially-occluded samples based on a proposed re-ID model and used these samples to further improve the performance of the model. The work in [10; 11; 14; 15] extracted numbers of discriminative characteristics, based on which end-to-end deep neural networks were trained. However, explicitly generating training samples and enlarging the datasets increase the burden on memory resources. It is feasible and efficient to enrich features on the feature map level when training networks. To this end, we propose a novel attention-aware adversarial network (AAA-Net) for person re-ID. Instead of explicitly enlarging the size of datasets, AAA-Net generates multiple occluded samples from each input on the feature map level. Specifically, the feature maps are blocked spatially, and a series of occluded feature maps is generated. Actually, this is an image-specific data augmentation method, which diversifies the features and provides sufficient training samples according to the input. Furthermore, an attention assignment mechanism is proposed to enforce attentions to be assigned to more important regions, as Figure 1, and thus, regions are served as convincing samples for the final classification results. Training the proposed framework consists of three stages. First, a baseline (BL) classifier for person re-ID is trained. Second, an attention assignment network is designed. According to the initial predicted attention map, as well as the set of occluded feature maps, attentions are further re-assigned to the rest of the interesting locations, besides the original focused area. This process is implemented in an adversarial strategy.