Deep Learning models like Convolutional Neural Networks (CNN) are powerful
image classifiers, but what factors determine whether they attend to similar
image areas as humans do? While previous studies have focused on technological
factors, little is known about the role of factors that affect human attention.
In the present study, we investigated how the tasks used to elicit human
attention maps interact with image characteristics in modulating the similarity
between humans and CNN. We varied the intentionality of human tasks, ranging
from spontaneous gaze during categorization over intentional gaze-pointing up
to manual area selection. Moreover, we varied the type of image to be
categorized, using either singular, salient objects, indoor scenes consisting
of object arrangements, or landscapes without distinct objects defining the
category. The human attention maps generated in this way were compared to the
CNN attention maps revealed by explainable artificial intelligence (Grad-CAM).
The influence of human tasks strongly depended on image type: For objects,
human manual selection produced maps that were most similar to CNN, while the
specific eye movement task has little impact. For indoor scenes, spontaneous
gaze produced the least similarity, while for landscapes, similarity was
equally low across all human tasks. To better understand these results, we also
compared the different human attention maps to each other. Our results
highlight the importance of taking human factors into account when comparing
the attention of humans and CNN.

通过研究人类与卷积神经网络 (CNN) 的注意力图之间的相似性，揭示了人类注意力的影响因素，发现任务类型和图像特征对人类与 CNN 的相似性产生显著影响，并强调在比较人类和 CNN 的注意力时需要考虑人类因素。

人类和卷积神经网络在场景分类时是否关注相似的区域：任务和图像类型的影响

Do humans and Convolutional Neural Networks attend to similar areas  during scene classification: Effects of task and image type

Nowadays, there is an explosive growth of screen contents due to the wide
application of screen sharing, remote cooperation, and online education. To
match the limited terminal bandwidth, high-resolution (HR) screen contents may
be downsampled and compressed. At the receiver side, the super-resolution (SR)
of low-resolution (LR) screen content images (SCIs) is highly demanded by the
HR display or by the users to zoom in for detail observation. However, image SR
methods mostly designed for natural images do not generalize well for SCIs due
to the very different image characteristics as well as the requirement of SCI
browsing at arbitrary scales. To this end, we propose a novel Implicit
Transformer Super-Resolution Network (ITSRN) for SCISR. For high-quality
continuous SR at arbitrary ratios, pixel values at query coordinates are
inferred from image features at key coordinates by the proposed implicit
transformer and an implicit position encoding scheme is proposed to aggregate
similar neighboring pixel values to the query one. We construct benchmark SCI1K
and SCI1K-compression datasets with LR and HR SCI pairs. Extensive experiments
show that the proposed ITSRN significantly outperforms several competitive
continuous and discrete SR methods for both compressed and uncompressed SCIs.

针对面向屏幕内容的超分辨率问题，本文提出一个新的 ITSRN 网络来实现高质量的连续超分辨率，该网络的关键在于调整像素的查询坐标并使用隐式位置编码方案对邻近像素值进行聚合，实验表明该方法在解决压缩和非压缩 SCI 方面优于其他 SR 方法。

屏幕内容图像连续超分辨率的隐式变换网络

Implicit Transformer Network for Screen Content Image Continuous  Super-Resolution

Most current super-resolution methods rely on low and high resolution image
pairs to train a network in a fully supervised manner. However, such image
pairs are not available in real-world applications. Instead of directly
addressing this problem, most works employ the popular bicubic downsampling
strategy to artificially generate a corresponding low resolution image.
Unfortunately, this strategy introduces significant artifacts, removing natural
sensor noise and other real-world characteristics. Super-resolution networks
trained on such bicubic images therefore struggle to generalize to natural
images. In this work, we propose an unsupervised approach for image
super-resolution. Given only unpaired data, we learn to invert the effects of
bicubic downsampling in order to restore the natural image characteristics
present in the data. This allows us to generate realistic image pairs,
faithfully reflecting the distribution of real-world images. Our
super-resolution network can therefore be trained with direct pixel-wise
supervision in the high resolution domain, while robustly generalizing to real
input. We demonstrate the effectiveness of our approach in quantitative and
qualitative experiments.

该论文提出了一种针对超分辨率图像的无监督方法，通过学习将人工生成的低分辨率图像的影响反演，从而还原数据中存在的自然图像特征，旨在解决使用人工生成的低分辨率图像所出现的问题。实验结果表明，该方法可以得到具有代表性的真实世界图像组，有效地提高超分辨率网络的泛化性。