Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Fusion-supervised Deep Cross-modal Hashing   Deep hashing has recently received attention in cross-modal retrieval for its
impressive advantages. However, existing hashing methods for cross-modal
retrieval cannot fully capture the heterogeneous multi-modal correlation and
exploit the semantic information. In this paper, we propose a novel
\emph{Fusion-supervised Deep Cross-modal Hashing} (FDCH) approach. Firstly,
FDCH learns unified binary codes through a fusion hash network with paired
samples as input, which effectively enhances the modeling of the correlation of
heterogeneous multi-modal data. Then, these high-quality unified hash codes
further supervise the training of the modality-specific hash networks for
encoding out-of-sample queries. Meanwhile, both pair-wise similarity
information and classification information are embedded in the hash networks
under one stream framework, which simultaneously preserves cross-modal
similarity and keeps semantic consistency. Experimental results on two
benchmark datasets demonstrate the state-of-the-art performance of FDCH.

7
The categories of this paper are: hep-th
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
3
The categories of this paper are: physics
17
The categories of this paper are: q-fin
16
The categories of this paper are: stat
26
The categories of this paper are: bayes-an
32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
9
The categories of this paper are: nlin
24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc