LAG-Net: Multi-Granularity Network for Person Re-Identification via Local Attention System

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Person re-identification (Re-ID) is a challenging research topic which aims to retrieve the pedestrian images of the same person that captured by non-overlapping cameras. Existing methods either assume the body parts of the same person are well-aligned, or use attention selection mechanisms to constrain the effective region of feature learning. But these methods concentrate only on coarse feature representation and cannot model complex real scenes effectively. We propose a novel Local Attention Guided Network (LAG-Net) to not only exploit the most salient area among different people, but also extract important local detail through a Local Attention System (LAS). LAS is an attention selection unit that could extract approximate semantic local features of human body parts without extra supervision. To learn discriminative attention feature representation, we explore an attention feature regularization scheme to enhance the relevance of body part features that belong to same personal identity. Considering the effectiveness of feature augmentation in the Re-ID task and the defect of the existing methods, we propose a Batch Attention DropBlock (BA-DropBlock) to further improve DropBlock by combining the attention selection mechanism. Results on mainstream datasets demonstrate the superiority of our model over the state-of-the-art. Especially, our approach exceeds the current best method by a large margin of 4.6% on the most challenging dataset CUHK03.

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IEEE Transactions on Multimedia

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