Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multimodal similarity-preserving hashing   We introduce an efficient computational framework for hashing data belonging
to multiple modalities into a single representation space where they become
mutually comparable. The proposed approach is based on a novel coupled siamese
neural network architecture and allows unified treatment of intra- and
inter-modality similarity learning. Unlike existing cross-modality similarity
learning approaches, our hashing functions are not limited to binarized linear
projections and can assume arbitrarily complex forms. We show experimentally
that our method significantly outperforms state-of-the-art hashing approaches
on multimedia retrieval tasks.

37
The categories of this paper are: supr-con
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
22
The categories of this paper are: chao-dyn