Deep learning (DL) is revolutionizing evidence-based decision-making techniques that can
be applied across various sectors. Specifically, it possesses the ability to utilize two or more …

A convolutional neural network (CNN) is one of the most significant networks in the deep
learning field. Since CNN made impressive achievements in many areas, including but not …

Effectively extracting inter-frame motion and appearance information is important for video
frame interpolation (VFI). Previous works either extract both types of information in a mixed …

Videos show continuous events, yet most--if not all--video synthesis frameworks treat them
discretely in time. In this work, we think of videos of what they should be--time-continuous …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

Dynamic neural network is an emerging research topic in deep learning. Compared to static
models which have fixed computational graphs and parameters at the inference stage …

Video restoration aims to restore high-quality frames from low-quality frames. Different from
single image restoration, video restoration generally requires to utilize temporal information …

We present a method to perform novel view and time synthesis of dynamic scenes, requiring
only a monocular video with known camera poses as input. To do this, we introduce Neural …

Prevailing video frame interpolation algorithms, that generate the intermediate frames from
consecutive inputs, typically rely on complex model architectures with heavy parameters or …

Real-time video frame interpolation (VFI) is very useful in video processing, media players,
and display devices. We propose RIFE, a Real-time Intermediate Flow Estimation algorithm …