This paper presents an overview of state-of-the-art methods in activity recognition using
semantic features. Unlike low-level features, semantic features describe inherent …

In the last decades, given the necessity of assisting fragile citizens, of which elderly
represent a significant portion, a considerable research effort has been devoted to the use of …

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 …

Most natural videos contain numerous events. For example, in a video of a" man playing a
piano", the video might also contain" another man dancing" or" a crowd clapping". We …

Every moment counts in action recognition. A comprehensive understanding of human
activity in video requires labeling every frame according to the actions occurring, placing …

This research tackles the problem of generating interaction between two human actors
corresponding to textual description. We claim that certain interactions, which we call …

Human action recognition under low observational latency is receiving a growing interest in
computer vision due to rapidly developing technologies in human-robot interaction …

We consider inferring the future actions of people from a still image or a short video clip.
Predicting future actions before they are actually executed is a critical ingredient for enabling …

In this work, we aim to address the problem of human interaction recognition in videos by
exploring the long-term inter-related dynamics among multiple persons. Recently, Long …

We present SkeleTR, a new framework for skeleton-based action recognition. In contrast to
prior work, which focuses mainly on controlled environments, we target in-the-wild scenarios …