  With the rapid growth of multimedia data (e.g., image, audio and video etc.)
on the web, learning-based hashing techniques such as Deep Supervised Hashing
(DSH) have proven to be very efficient for large-scale multimedia search. The
recent successes seen in Learning-based hashing methods are largely due to the
success of deep learning-based hashing methods. However, there are some
limitations to previous learning-based hashing methods (e.g., the learned hash
codes containing repetitive and highly correlated information). In this paper,
we propose a novel learning-based hashing method, named Deep Attention-guided
Hashing (DAgH). DAgH is implemented using two stream frameworks. The core idea
is to use guided hash codes which are generated by the hashing network of the
first stream framework (called first hashing network) to guide the training of
the hashing network of the second stream framework (called second hashing
network). Specifically, in the first network, it leverages an attention network
and hashing network to generate the attention-guided hash codes from the
original images. The loss function we propose contains two components: the
semantic loss and the attention loss. The attention loss is used to punish the
attention network to obtain the salient region from pairs of images; in the
second network, these attention-guided hash codes are used to guide the
training of the second hashing network (i.e., these codes are treated as
supervised labels to train the second network). By doing this, DAgH can make
full use of the most critical information contained in images to guide the
second hashing network in order to learn efficient hash codes in a true
end-to-end fashion. Results from our experiments demonstrate that DAgH can
generate high quality hash codes and it outperforms current state-of-the-art
methods on three benchmark datasets, CIFAR-10, NUS-WIDE, and ImageNet.
