Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Camera Style Adaptation for Person Re-identification   Being a cross-camera retrieval task, person re-identification suffers from
image style variations caused by different cameras. The art implicitly
addresses this problem by learning a camera-invariant descriptor subspace. In
this paper, we explicitly consider this challenge by introducing camera style
(CamStyle) adaptation. CamStyle can serve as a data augmentation approach that
smooths the camera style disparities. Specifically, with CycleGAN, labeled
training images can be style-transferred to each camera, and, along with the
original training samples, form the augmented training set. This method, while
increasing data diversity against over-fitting, also incurs a considerable
level of noise. In the effort to alleviate the impact of noise, the label
smooth regularization (LSR) is adopted. The vanilla version of our method
(without LSR) performs reasonably well on few-camera systems in which
over-fitting often occurs. With LSR, we demonstrate consistent improvement in
all systems regardless of the extent of over-fitting. We also report
competitive accuracy compared with the state of the art.

31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
15
The categories of this paper are: nucl-ex
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
19
The categories of this paper are: eess
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
10
The categories of this paper are: q-bio
16
The categories of this paper are: stat
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat