Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Domain Adaptation through Synthesis for Unsupervised Person
  Re-identification   Drastic variations in illumination across surveillance cameras make the
person re-identification problem extremely challenging. Current large scale
re-identification datasets have a significant number of training subjects, but
lack diversity in lighting conditions. As a result, a trained model requires
fine-tuning to become effective under an unseen illumination condition. To
alleviate this problem, we introduce a new synthetic dataset that contains
hundreds of illumination conditions. Specifically, we use 100 virtual humans
illuminated with multiple HDR environment maps which accurately model realistic
indoor and outdoor lighting. To achieve better accuracy in unseen illumination
conditions we propose a novel domain adaptation technique that takes advantage
of our synthetic data and performs fine-tuning in a completely unsupervised
way. Our approach yields significantly higher accuracy than semi-supervised and
unsupervised state-of-the-art methods, and is very competitive with supervised
techniques.

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The categories of this paper are: comp-gas
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
26
The categories of this paper are: bayes-an
34
The categories of this paper are: chem-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
17
The categories of this paper are: q-fin
32
The categories of this paper are: atom-ph
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
35
The categories of this paper are: mtrl-th
36
The categories of this paper are: cmp-lg