  Since the person re-identification task often suffers from the problem of
pose changes and occlusions, some attentive local features are often suppressed
when training CNNs. In this paper, we propose the Batch DropBlock (BDB) Network
which is a two branch network composed of a conventional ResNet-50 as the
global branch and a feature dropping branch. The global branch encodes the
global salient representations. Meanwhile, the feature dropping branch consists
of an attentive feature learning module called Batch DropBlock, which randomly
drops the same region of all input feature maps in a batch to reinforce the
attentive feature learning of local regions. The network then concatenates
features from both branches and provides a more comprehensive and spatially
distributed feature representation. Albeit simple, our method achieves
state-of-the-art on person re-identification and it is also applicable to
general metric learning tasks. For instance, we achieve 76.4% Rank-1 accuracy
on the CUHK03-Detect dataset and 83.0% Recall-1 score on the Stanford Online
Products dataset, outperforming the existing works by a large margin (more than
6%).
