Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Exploring Correlations in Multiple Facial Attributes through Graph
  Attention Network   Estimating multiple attributes from a single facial image gives comprehensive
descriptions on the high level semantics of the face. It is naturally regarded
as a multi-task supervised learning problem with a single deep CNN, in which
lower layers are shared, and higher ones are task-dependent with the
multi-branch structure. Within the traditional deep multi-task learning (DMTL)
framework, this paper intends to fully exploit the correlations among different
attributes by constructing a graph. The node in graph represents the feature
vector from a particular branch for a given attribute, and the edge can be
defined by either the prior knowledge or the similarity between two nodes in
the embedding with a fully data-driven manner. We analyze that the attention
mechanism actually takes effect in the latter case, and utilize the Graph
Attention Layer (GAL) for exploring on the most relevant attribute feature and
refining the task-dependant feature by considering other attributes.
Experiments show that by mining the correlations among attributes, our method
can improve the recognition accuracy on CelebA and LFWA dataset. And it also
achieves competitive performance.

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The categories of this paper are: atom-ph
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The categories of this paper are: eess
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
27
The categories of this paper are: comp-gas
9
The categories of this paper are: nlin
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
11
The categories of this paper are: hep-lat
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The categories of this paper are: chem-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: acc-phys
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The categories of this paper are: econ
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The categories of this paper are: alg-geom