Derived from rapid advances in computer vision and machine learning, video analysis tasks
have been moving from inferring the present state to predicting the future state. Vision-based …

Recently, 3D Convolutional Neural Network (3D CNN) models have been widely studied for
video sequences and achieved satisfying performance in action recognition and detection …

To meet the demand for powerful models for practical applications in real time, the focus of
research on human actions has shifted from offline detection to online and real-time …

The temporal action localization research aims to discover action instances from untrimmed
videos, representing a fundamental step in the field of intelligent video understanding. With …

Video question answering (VideoQA) has emerged as a popular research topic in recent
years. Enormous efforts have been devoted to developing more effective fusion strategies …

We propose a novel method of exploiting informative video segments by learning segment
weights for temporal action localization in untrimmed videos. Informative video segments …

One major challenge of Weakly-supervised Temporal Action Localization (WTAL) is to
handle diverse backgrounds in videos. To model background frames, most existing methods …

Given a natural language description, temporal textual localization aims to localize the most
relevant segment in an untrimmed video, which is a natural and imperative extension of …

Weakly-supervised Temporal Action Localization (W-TAL) aims at simultaneously classifying
and locating all action instances with only video-level supervision. However, current W-TAL …

The analysis of teacher behavior of massive teaching videos has become a surge of
research interest recently. Traditional methods rely on accurate manual analysis, which is …