Video super-resolution plays an important role in surveillance video analysis
and ultra-high-definition video display, which has drawn much attention in both
the research and industrial communities. Although many deep learning-based VSR
methods have been proposed, it is hard to directly compare these methods since
the different loss functions and training datasets have a significant impact on
the super-resolution results. In this work, we carefully study and compare
three temporal modeling methods (2D CNN with early fusion, 3D CNN with slow
fusion and Recurrent Neural Network) for video super-resolution. We also
propose a novel Recurrent Residual Network (RRN) for efficient video
super-resolution, where residual learning is utilized to stabilize the training
of RNN and meanwhile to boost the super-resolution performance. Extensive
experiments show that the proposed RRN is highly computational efficiency and
produces temporal consistent VSR results with finer details than other temporal
modeling methods. Besides, the proposed method achieves state-of-the-art
results on several widely used benchmarks.

本文提出了一种新颖的循环残差网络模型用于视频超分辨率，该模型相比于其他几种建模方法具有更高的计算效率和更好的超分辨率效果，取得了当前最好的结果。

视频超分辨率的时间建模再思考

Revisiting Temporal Modeling for Video Super-resolution

Video-based person reID is an important task, which has received much
attention in recent years due to the increasing demand in surveillance and
camera networks. A typical video-based person reID system consists of three
parts: an image-level feature extractor (e.g. CNN), a temporal modeling method
to aggregate temporal features and a loss function. Although many methods on
temporal modeling have been proposed, it is hard to directly compare these
methods, because the choice of feature extractor and loss function also have a
large impact on the final performance. We comprehensively study and compare
four different temporal modeling methods (temporal pooling, temporal attention,
RNN and 3D convnets) for video-based person reID. We also propose a new
attention generation network which adopts temporal convolution to extract
temporal information among frames. The evaluation is done on the MARS dataset,
and our methods outperform state-of-the-art methods by a large margin. Our
source codes are released at this https URL

该文综合研究和比较了视频人员重识别的四种不同的时间建模方法，包括时间汇聚，时间关注，RNN 和 3D 卷积神经网络，并提出了一个采用时间卷积的注意力生成网络，用于在帧之间提取时间信息。在 MARS 数据集上进行评估，并通过一大部分超越了最新技术的方法。