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 …

The main challenge in the field of unsupervised machine translation (UMT) is to associate
source-target sentences in the latent space. As people who speak different languages share …

Abstract Video Semantic Segmentation (VSS) involves assigning a semantic label to each
pixel in a video sequence. Prior work in this field has demonstrated promising results by …

In this paper, we propose a new, simple, and effective Self-supervised Spatio-temporal
Transformers (SPARTAN) approach to Group Activity Recognition (GAR) using unlabeled …

The task of action detection aims at deducing both the action category and localization of the
start and end moment for each action instance in a long, untrimmed video. While vision …

This paper proposes joint attention estimation in a single image. Different from related work
in which only the gaze-related attributes of people are independently employed,(I) their …

Deep learning has the potential to revolutionize sports performance, with applications
ranging from perception and comprehension to decision. This paper presents a …

Early group activity recognition is typically conducted in a 2D scenario. This paper proposes
a group activity recognition method in a 3D space. In practical applications, differences in …

Group activity recognition, which aims to simultaneously understand individual action and
group activity in video clips, plays a fundamental role in computer vision and video analysis …

This paper presents a weakly supervised approach for group activity recognition by
exploiting the local relative motion and global correlations among entities. Most existing …