Human Action Recognition (HAR) aims to understand human behavior and assign a label to
each action. It has a wide range of applications, and therefore has been attracting increasing …

Automated surveillance systems observe the environment utilizing cameras. The observed
scenario is then analysed using motion detection, crowd behaviour, individual behaviour …

Video understanding requires reasoning at multiple spatiotemporal resolutions--from short
fine-grained motions to events taking place over longer durations. Although transformer …

We present pure-transformer based models for video classification, drawing upon the recent
success of such models in image classification. Our model extracts spatio-temporal tokens …

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 …

Temporal modeling is key for action recognition in videos. It normally considers both short-
range motions and long-range aggregations. In this paper, we propose a Temporal …

Abstract Deep Convolutional Neural Network (CNN) is a special type of Neural Networks,
which has shown exemplary performance on several competitions related to Computer …

The explosive growth in video streaming gives rise to challenges on performing video
understanding at high accuracy and low computation cost. Conventional 2D CNNs are …

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 …

We propose a self-supervised spatiotemporal learning technique which leverages the
chronological order of videos. Our method can learn the spatiotemporal representation of …