Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Two-pronged Strategy: Lightweight Augmented Graph Network Hashing for
  Scalable Image Retrieval   Hashing learns compact binary codes to store and retrieve massive data
efficiently. Particularly, unsupervised deep hashing is supported by powerful
deep neural networks and has the desirable advantage of label independence. It
is a promising technique for scalable image retrieval. However, deep models
introduce a large number of parameters, which is hard to optimize due to the
lack of explicit semantic labels and brings considerable training cost. As a
result, the retrieval accuracy and training efficiency of existing unsupervised
deep hashing are still limited. To tackle the problems, in this paper, we
propose a simple and efficient \emph{Lightweight Augmented Graph Network
Hashing} (LAGNH) method with a two-pronged strategy. For one thing, we extract
the inner structure of the image as the auxiliary semantics to enhance the
semantic supervision of the unsupervised hash learning process. For another, we
design a lightweight network structure with the assistance of the auxiliary
semantics, which greatly reduces the number of network parameters that needs to
be optimized and thus greatly accelerates the training process. Specifically,
we design a cross-modal attention module based on the auxiliary semantic
information to adaptively mitigate the adverse effects in the deep image
features. Besides, the hash codes are learned by multi-layer message passing
within an adversarial regularized graph convolutional network. Simultaneously,
the semantic representation capability of hash codes is further enhanced by
reconstructing the similarity graph.

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The categories of this paper are: atom-ph
20
The categories of this paper are: acc-phys
10
The categories of this paper are: q-bio
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The categories of this paper are: mtrl-th
2
The categories of this paper are: math
11
The categories of this paper are: hep-lat
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
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The categories of this paper are: eess
8
The categories of this paper are: hep-ex
18
The categories of this paper are: econ
9
The categories of this paper are: nlin
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The categories of this paper are: adap-org
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The categories of this paper are: patt-sol