Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Zero Shot Hashing   This paper provides a framework to hash images containing instances of
unknown object classes. In many object recognition problems, we might have
access to huge amount of data. It may so happen that even this huge data
doesn't cover the objects belonging to classes that we see in our day to day
life. Zero shot learning exploits auxiliary information (also called as
signatures) in order to predict the labels corresponding to unknown classes. In
this work, we attempt to generate the hash codes for images belonging to unseen
classes, information of which is available only through the textual corpus. We
formulate this as an unsupervised hashing formulation as the exact labels are
not available for the instances of unseen classes. We show that the proposed
solution is able to generate hash codes which can predict labels corresponding
to unseen classes with appreciably good precision.

32
The categories of this paper are: atom-ph
25
The categories of this paper are: patt-sol
31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
23
The categories of this paper are: solv-int
33
The categories of this paper are: plasm-ph
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom