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.
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Liu, Z., Zhang, L. (2019). SliceNet: Mask Guided Efficient Feature Augmentation for Attention-Aware Person Re-Identification. In: Cui, Z., Pan, J., Zhang, S., Xiao, L., Yang, J. (eds) Intelligence Science and Big Data Engineering. Visual Data Engineering. IScIDE 2019. Lecture Notes in Computer Science(), vol 11935. Springer, Cham. https://doi.org/10.1007/978-3-030-36189-1_8
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