Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Domain Adaptive Person Re-Identification via Human Learning
  Imitation   Unsupervised domain adaptive person re-identification has received
significant attention due to its high practical value. In past years, by
following the clustering and finetuning paradigm, researchers propose to
utilize the teacher-student framework in their methods to decrease the domain
gap between different person re-identification datasets. Inspired by recent
teacher-student framework based methods, which try to mimic the human learning
process either by making the student directly copy behavior from the teacher or
selecting reliable learning materials, we propose to conduct further
exploration to imitate the human learning process from different aspects,
\textit{i.e.}, adaptively updating learning materials, selectively imitating
teacher behaviors, and analyzing learning materials structures. The explored
three components, collaborate together to constitute a new method for
unsupervised domain adaptive person re-identification, which is called Human
Learning Imitation framework. The experimental results on three benchmark
datasets demonstrate the efficacy of our proposed method.

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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: adap-org
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The categories of this paper are: solv-int
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The categories of this paper are: q-bio
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The categories of this paper are: nlin
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The categories of this paper are: comp-gas
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The categories of this paper are: math
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The categories of this paper are: physics
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The categories of this paper are: bayes-an
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The categories of this paper are: supr-con
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The categories of this paper are: econ
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The categories of this paper are: hep-ph
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The categories of this paper are: hep-ex
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The categories of this paper are: plasm-ph