Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Task Specific Visual Saliency Prediction with Memory Augmented
  Conditional Generative Adversarial Networks   Visual saliency patterns are the result of a variety of factors aside from
the image being parsed, however existing approaches have ignored these. To
address this limitation, we propose a novel saliency estimation model which
leverages the semantic modelling power of conditional generative adversarial
networks together with memory architectures which capture the subject's
behavioural patterns and task dependent factors. We make contributions aiming
to bridge the gap between bottom-up feature learning capabilities in modern
deep learning architectures and traditional top-down hand-crafted features
based methods for task specific saliency modelling. The conditional nature of
the proposed framework enables us to learn contextual semantics and
relationships among different tasks together, instead of learning them
separately for each task. Our studies not only shed light on a novel
application area for generative adversarial networks, but also emphasise the
importance of task specific saliency modelling and demonstrate the plausibility
of fully capturing this context via an augmented memory architecture.

16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
15
The categories of this paper are: nucl-ex
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
22
The categories of this paper are: chao-dyn
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
32
The categories of this paper are: atom-ph