Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cross-Dataset Person Re-Identification via Unsupervised Pose
  Disentanglement and Adaptation   Person re-identification (re-ID) aims at recognizing the same person from
images taken across different cameras. To address this challenging task,
existing re-ID models typically rely on a large amount of labeled training
data, which is not practical for real-world applications. To alleviate this
limitation, researchers now targets at cross-dataset re-ID which focuses on
generalizing the discriminative ability to the unlabeled target domain when
given a labeled source domain dataset. To achieve this goal, our proposed Pose
Disentanglement and Adaptation Network (PDA-Net) aims at learning deep image
representation with pose and domain information properly disentangled. With the
learned cross-domain pose invariant feature space, our proposed PDA-Net is able
to perform pose disentanglement across domains without supervision in
identities, and the resulting features can be applied to cross-dataset re-ID.
Both of our qualitative and quantitative results on two benchmark datasets
confirm the effectiveness of our approach and its superiority over the
state-of-the-art cross-dataset Re-ID approaches.

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The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
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The categories of this paper are: math-ph
3
The categories of this paper are: physics
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The categories of this paper are: patt-sol
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The categories of this paper are: econ
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The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
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The categories of this paper are: atom-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: astro-ph
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The categories of this paper are: hep-ph