  Recently, Batch DropBlock network (BDB) has demonstrated its effectiveness on
person image representation and re-identification task via feature erasing.
However, BDB drops the features \textbf{randomly} which may lead to sub-optimal
results. In this paper, we propose a novel Self-Thresholding attention guided
Adaptive DropBlock network (STADB) for person re-ID which can
\textbf{adaptively} erase the most discriminative regions. Specifically, STADB
first obtains an attention map by channel-wise pooling and returns a drop mask
by thresholding the attention map. Then, the input features and
self-thresholding attention guided drop mask are multiplied to generate the
dropped feature maps. In addition, STADB utilizes the spatial and channel
attention to learn a better feature map and iteratively trains the feature
dropping module for person re-ID. Experiments on several benchmark datasets
demonstrate that the proposed STADB outperforms many other related methods for
person re-ID. The source code of this paper is released at:
\textcolor{red}{\url{https://github.com/wangxiao5791509/STADB_ReID}}.
