Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Simultaneous Region Localization and Hash Coding for Fine-grained Image
  Retrieval   Fine-grained image hashing is a challenging problem due to the difficulties
of discriminative region localization and hash code generation. Most existing
deep hashing approaches solve the two tasks independently. While these two
tasks are correlated and can reinforce each other. In this paper, we propose a
deep fine-grained hashing to simultaneously localize the discriminative regions
and generate the efficient binary codes. The proposed approach consists of a
region localization module and a hash coding module. The region localization
module aims to provide informative regions to the hash coding module. The hash
coding module aims to generate effective binary codes and give feedback for
learning better localizer. Moreover, to better capture subtle differences,
multi-scale regions at different layers are learned without the need of
bounding-box/part annotations. Extensive experiments are conducted on two
public benchmark fine-grained datasets. The results demonstrate significant
improvements in the performance of our method relative to other fine-grained
hashing algorithms.

27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
29
The categories of this paper are: funct-an
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
21
The categories of this paper are: adap-org
2
The categories of this paper are: math
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th