Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Spatial Attention for Face Super-Resolution   General image super-resolution techniques have difficulties in recovering
detailed face structures when applying to low resolution face images. Recent
deep learning based methods tailored for face images have achieved improved
performance by jointly trained with additional task such as face parsing and
landmark prediction. However, multi-task learning requires extra manually
labeled data. Besides, most of the existing works can only generate relatively
low resolution face images (e.g., $128\times128$), and their applications are
therefore limited. In this paper, we introduce a novel SPatial Attention
Residual Network (SPARNet) built on our newly proposed Face Attention Units
(FAUs) for face super-resolution. Specifically, we introduce a spatial
attention mechanism to the vanilla residual blocks. This enables the
convolutional layers to adaptively bootstrap features related to the key face
structures and pay less attention to those less feature-rich regions. This
makes the training more effective and efficient as the key face structures only
account for a very small portion of the face image. Visualization of the
attention maps shows that our spatial attention network can capture the key
face structures well even for very low resolution faces (e.g., $16\times16$).
Quantitative comparisons on various kinds of metrics (including PSNR, SSIM,
identity similarity, and landmark detection) demonstrate the superiority of our
method over current state-of-the-arts. We further extend SPARNet with
multi-scale discriminators, named as SPARNetHD, to produce high resolution
results (i.e., $512\times512$). We show that SPARNetHD trained with synthetic
data cannot only produce high quality and high resolution outputs for
synthetically degraded face images, but also show good generalization ability
to real world low quality face images.

24
The categories of this paper are: dg-ga
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
31
The categories of this paper are: ao-sci
18
The categories of this paper are: econ
3
The categories of this paper are: physics
19
The categories of this paper are: eess
21
The categories of this paper are: adap-org
32
The categories of this paper are: atom-ph
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg
35
The categories of this paper are: mtrl-th
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th