Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: STADB: A Self-Thresholding Attention Guided ADB Network for Person
  Re-identification   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}}.

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The categories of this paper are: bayes-an
9
The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: mtrl-th
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The categories of this paper are: plasm-ph
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The categories of this paper are: econ
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The categories of this paper are: alg-geom
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The categories of this paper are: patt-sol
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The categories of this paper are: quant-ph
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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: solv-int
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The categories of this paper are: stat