Video-based human action recognition is one of the most important and challenging areas
of research in the field of computer vision. Human action recognition has found many …

Skeleton-based action recognition has rapidly become one of the most popular and
essential research topics in computer vision. The task is to analyze the characteristics of …

We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural
network architecture for learning optical flow. FlowFormer tokenizes the 4D cost volume built …

FlowFormer introduces a transformer architecture into optical flow estimation and achieves
state-of-the-art performance. The core component of FlowFormer is the transformer-based …

This paper presents X3D, a family of efficient video networks that progressively expand a
tiny 2D image classification architecture along multiple network axes, in space, time, width …

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 …

Human activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …

Optical flow estimation aims to find the 2D motion field by identifying corresponding pixels
between two images. Despite the tremendous progress of deep learning-based optical flow …

Spatiotemporal and motion features are two complementary and crucial information for
video action recognition. Recent state-of-the-art methods adopt a 3D CNN stream to learn …

Video action recognition is one of the representative tasks for video understanding. Over the
last decade, we have witnessed great advancements in video action recognition thanks to …