SliceNet: Mask Guided Efficient Feature Augmentation for Attention-Aware Person Re-Identification

Abstract

Person re-identification (re-ID) is a challenging task since the same person captured by different cameras can appear very differently, due to the uncontrolled factors such as occlusion, illumination, viewpoint and pose variation etc. Attention-based person re-ID methods have been extensively studied to focus on discriminative regions of the last convolutional layer, which, however, ignore the low-level fine-grained information. In this paper, we propose a novel SliceNet with efficient feature augmentation modules for open-world person re-identification. Specifically, with the philosophy of divide and conquer, we divide the baseline network into three sub-networks from low, middle and high levels, which are called slice networks, followed by a Self-Alignment Attention Module respectively to learn multi-level discriminative parts. In contrast with existing works that uniformly partition the images into multiple patches, our attention module aims to learn self-alignment masks for discovering and exploiting the align-attention regions. Further, SliceNet is combined with the attention free baseline network to characterize global features. Extensive experiments on the benchmark datasets including Market-1501, CUHK03, and DukeMTMC-reID show that our proposed SliceNet achieves favorable performance compared with the state-of-the art methods.