Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Re-ID done right: towards good practices for person re-identification   Training a deep architecture using a ranking loss has become standard for the
person re-identification task. Increasingly, these deep architectures include
additional components that leverage part detections, attribute predictions,
pose estimators and other auxiliary information, in order to more effectively
localize and align discriminative image regions. In this paper we adopt a
different approach and carefully design each component of a simple deep
architecture and, critically, the strategy for training it effectively for
person re-identification. We extensively evaluate each design choice, leading
to a list of good practices for person re-identification. By following these
practices, our approach outperforms the state of the art, including more
complex methods with auxiliary components, by large margins on four benchmark
datasets. We also provide a qualitative analysis of our trained representation
which indicates that, while compact, it is able to capture information from
localized and discriminative regions, in a manner akin to an implicit attention
mechanism.

28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
10
The categories of this paper are: q-bio
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
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The categories of this paper are: solv-int
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The categories of this paper are: q-fin
13
The categories of this paper are: quant-ph
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
3
The categories of this paper are: physics
0
The categories of this paper are: hep-ph