For video super-resolution, current state-of-the-art approaches either
process multiple low-resolution (LR) frames to produce each output
high-resolution (HR) frame separately in a sliding window fashion or
recurrently exploit the previously estimated HR frames to super-resolve the
following frame. The main weaknesses of these approaches are: 1) separately
generating each output frame may obtain high-quality HR estimates while
resulting in unsatisfactory flickering artifacts, and 2) combining previously
generated HR frames can produce temporally consistent results in the case of
short information flow, but it will cause significant jitter and jagged
artifacts because the previous super-resolving errors are constantly
accumulated to the subsequent frames. In this paper, we propose a fully
end-to-end trainable frame and feature-context video super-resolution (FFCVSR)
network that consists of two key sub-networks: local network and context
network, where the first one explicitly utilizes a sequence of consecutive LR
frames to generate local feature and local SR frame, and the other combines the
outputs of local network and the previously estimated HR frames and features to
super-resolve the subsequent frame. Our approach takes full advantage of the
inter-frame information from multiple LR frames and the context information
from previously predicted HR frames, producing temporally consistent
high-quality results while maintaining real-time speed by directly reusing
previous features and frames. Extensive evaluations and comparisons demonstrate
that our approach produces state-of-the-art results on a standard benchmark
dataset, with advantages in terms of accuracy, efficiency, and visual quality
over the existing approaches.

本文提出一个全新的基于上下文信息的可训练视频超分辨率网络，结合了局部网络和上下文网络，通过利用多个低分辨率帧中的信息和之前预测的高分辨率帧的特征来保持时域连续性，从而实现高质量视频超分辨率结果的实时生成，结果表明本文方法在准确性、效率和视觉质量等方面均优于现有方法。

框架和特征上下文视频超分辨率

Frame and Feature-Context Video Super-Resolution

Generating realistic images from scene graphs asks neural networks to be able
to reason about object relationships and compositionality. As a relatively new
task, how to properly ensure the generated images comply with scene graphs or
how to measure task performance remains an open question. In this paper, we
propose to harness scene graph context to improve image generation from scene
graphs. We introduce a scene graph context network that pools features
generated by a graph convolutional neural network that are then provided to
both the image generation network and the adversarial loss. With the context
network, our model is trained to not only generate realistic looking images,
but also to better preserve non-spatial object relationships. We also define
two novel evaluation metrics, the relation score and the mean opinion relation
score, for this task that directly evaluate scene graph compliance. We use both
quantitative and qualitative studies to demonstrate that our pro-posed model
outperforms the state-of-the-art on this challenging task.

本研究提出使用场景图上下文来改善场景图的图像生成，通过引入上下文网络将图卷积神经网络生成的特征并入图像生成网络和对抗性损失，从而不仅能够生成逼真的图像，还能更好地保留非空间对象之间的关系，并定义两个评估指标，Relation Score 和 Mean Opinion Relation Score，直接评估场景图的一致性，并经过定量和定性研究证明了该模型在这一具有挑战性的任务上优于现有技术。

利用场景图上下文改进图像生成

Using Scene Graph Context to Improve Image Generation

We present a scene parsing method that utilizes global context information
based on both the parametric and non- parametric models. Compared to previous
methods that only exploit the local relationship between objects, we train a
context network based on scene similarities to generate feature representations
for global contexts. In addition, these learned features are utilized to
generate global and spatial priors for explicit classes inference. We then
design modules to embed the feature representations and the priors into the
segmentation network as additional global context cues. We show that the
proposed method can eliminate false positives that are not compatible with the
global context representations. Experiments on both the MIT ADE20K and PASCAL
Context datasets show that the proposed method performs favorably against
existing methods.

本文提出了基于参数化和非参数化模型的全局语境信息来进行场景解析，使用场景相似性训练上下文网络生成特征表示来生成空间和全局先验知识，然后将这些特征表示和先验知识嵌入到分割网络作为额外的全局上下文提示。实验表明该方法可以消除与全局上下文表示不兼容的误报，且在 MIT ADE20K 和 PASCAL Context 数据集上表现良好。