Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Adaptation and Re-Identification Network: An Unsupervised Deep Transfer
  Learning Approach to Person Re-Identification   Person re-identification (Re-ID) aims at recognizing the same person from
images taken across different cameras. To address this task, one typically
requires a large amount labeled data for training an effective Re-ID model,
which might not be practical for real-world applications. To alleviate this
limitation, we choose to exploit a sufficient amount of pre-existing labeled
data from a different (auxiliary) dataset. By jointly considering such an
auxiliary dataset and the dataset of interest (but without label information),
our proposed adaptation and re-identification network (ARN) performs
unsupervised domain adaptation, which leverages information across datasets and
derives domain-invariant features for Re-ID purposes. In our experiments, we
verify that our network performs favorably against state-of-the-art
unsupervised Re-ID approaches, and even outperforms a number of baseline Re-ID
methods which require fully supervised data for training.

8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
37
The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org
30
The categories of this paper are: q-alg
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
23
The categories of this paper are: solv-int
4
The categories of this paper are: cond-mat
19
The categories of this paper are: eess
22
The categories of this paper are: chao-dyn
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
20
The categories of this paper are: acc-phys