Single image super-resolution (SISR) aims to recover the high-resolution (HR)
image from its low-resolution (LR) input image. With the development of deep
learning, SISR has achieved great progress. However, It is still a challenge to
restore the real-world LR image with complicated authentic degradations.
Therefore, we propose FAN, a frequency aggregation network, to address the
real-world image super-resolu-tion problem. Specifically, we extract different
frequencies of the LR image and pass them to a channel attention-grouped
residual dense network (CA-GRDB) individually to output corresponding feature
maps. And then aggregating these residual dense feature maps adaptively to
recover the HR image with enhanced details and textures. We conduct extensive
experiments quantitatively and qualitatively to verify that our FAN performs
well on the real image super-resolution task of AIM 2020 challenge. According
to the released final results, our team SR-IM achieves the fourth place on the
X4 track with PSNR of 31.1735 and SSIM of 0.8728.

提出了一种名为 FAN 的频率聚合网络方法，它能够针对复杂的现实降解恢复真实世界低分辨率图像的高分辨率版本，通过提取不同频率的低分辨率图像，并分别传递到一种称为 CA-GRDB 的通道注意力聚合残差密集网络中，最后自适应地聚合这些残余特征图来恢复具有增强细节和纹理的高分辨率图像，实验证明该方法在 AIM2020 挑战赛的真实图像超分辨率任务上表现出色，最终权衡指标 PSNR 达到 31.1735，SSIM 达到 0.8728，我们的团队 SR-IM 获得该赛道的第四名。

基于频率聚合的真实图像超分辨率网络

FAN: Frequency Aggregation Network for Real Image Super-resolution

Visual object tracking is an important task that requires the tracker to find
the objects quickly and accurately. The existing state-ofthe-art object
trackers, i.e., Siamese based trackers, use DNNs to attain high accuracy.
However, the robustness of visual tracking models is seldom explored. In this
paper, we analyze the weakness of object trackers based on the Siamese network
and then extend adversarial examples to visual object tracking. We present an
end-to-end network FAN (Fast Attack Network) that uses a novel drift loss
combined with the embedded feature loss to attack the Siamese network based
trackers. Under a single GPU, FAN is efficient in the training speed and has a
strong attack performance. The FAN can generate an adversarial example at 10ms,
achieve effective targeted attack (at least 40% drop rate on OTB) and
untargeted attack (at least 70% drop rate on OTB).

本文分析了基于 Siamese 网络的物体跟踪器的脆弱性，提出了一种名为 FAN 的端到端网络，使用漂移损失和嵌入特征损失来攻击 Siamese 网络跟踪器，并在单个 GPU 上具有高效的训练速度和强大的攻击性能，可以在 10 毫秒内生成对抗性样本，并获得有效的有针对性和非有针对性攻击。

视觉目标跟踪的高效对抗攻击

Efficient Adversarial Attacks for Visual Object Tracking

Face Super-Resolution (SR) is a subfield of the SR domain that specifically
targets the reconstruction of face images. The main challenge of face SR is to
restore essential facial features without distortion. We propose a novel face
SR method that generates photo-realistic 8x super-resolved face images with
fully retained facial details. To that end, we adopt a progressive training
method, which allows stable training by splitting the network into successive
steps, each producing output with a progressively higher resolution. We also
propose a novel facial attention loss and apply it at each step to focus on
restoring facial attributes in greater details by multiplying the pixel
difference and heatmap values. Lastly, we propose a compressed version of the
state-of-the-art face alignment network (FAN) for landmark heatmap extraction.
With the proposed FAN, we can extract the heatmaps suitable for face SR and
also reduce the overall training time. Experimental results verify that our
method outperforms state-of-the-art methods in both qualitative and
quantitative measurements, especially in perceptual quality.

本论文提出了一种新颖的面部高清晰度恢复方法，采用了渐进式训练、面部注意力损失和压缩的人脸标注热图提取网络，实现了全面的面部特征恢复，使得图像更为逼真清晰，表现最好。