SaADB: A Self-attention Guided ADB Network for Person Re-identification

Recently, Batch DropBlock network (BDB) has demonstrated its effectiveness on person image representation and re-ID task via feature erasing. However, BDB drops the features randomly which may lead to sub-optimal results. In this paper, we propose a novel Self-attention guided Adaptive DropBlock network (SaADB) for person re-ID which can adaptively erase the most discriminative regions. Specifically, SaADB first obtains a self-attention map by channel-wise pooling and returns a drop mask by thresholding the self-attention map. Then, the input features and self-attention guided drop mask are multiplied to generate the dropped feature maps. Meanwhile, we utilize the spatial and channel attention to learn a better feature map and iteratively train with the feature dropping module for person re-ID. Experiments on several benchmark datasets demonstrate that the proposed SaADB significantly beats the prevalent competitors in person re-ID.