Multimodal machine learning is a vibrant multi-disciplinary research field that aims to design
computer agents with intelligent capabilities such as understanding, reasoning, and learning …

Sign language, as a different form of the communication language, is important to large
groups of people in society. There are different signs in each sign language with variability …

This paper introduces an enormous dataset, HaGRID (HAnd Gesture Recognition Image
Dataset), to build a hand gesture recognition (HGR) system concentrating on interaction with …

Skeleton-based human action recognition has attracted a lot of research attention during the
past few years. Recent works attempted to utilize recurrent neural networks to model the …

Recently, skeleton based action recognition gains more popularity due to cost-effective
depth sensors coupled with real-time skeleton estimation algorithms. Traditional approaches …

The objective of this work is human pose estimation in videos, where multiple frames are
available. We investigate a ConvNet architecture that is able to benefit from temporal context …

In this paper we present a method to capture video-wide temporal information for action
recognition. We postulate that a function capable of ordering the frames of a video …

Temporal dynamics of postures over time is crucial for sequence-based action recognition.
Human actions can be represented by the corresponding motions of articulated skeleton …

The concept of intelligent visual identification of abnormal human activity has raised the
standards of surveillance systems, situation cognizance, homeland safety and smart …

Successful efforts in hand gesture recognition research within the last two decades paved
the path for natural human–computer interaction systems. Unresolved challenges such as …