Temporal action segmentation (TAS) in videos aims at densely identifying video frames in
minutes-long videos with multiple action classes. As a long-range video understanding task …

Human activity recognition (HAR) systems attempt to automatically identify and analyze
human activities using acquired information from various types of sensors. Although several …

Algorithms for the action segmentation task typically use temporal models to predict what
action is occurring at each frame for a minute-long daily activity. Recent studies have shown …

Temporally locating and classifying action segments in long untrimmed videos is of
particular interest to many applications like surveillance and robotics. While traditional …

This paper introduces a unified framework for video action segmentation via sequence to
sequence (seq2seq) translation in a fully and timestamp supervised setup. In contrast to …

The ability to identify and temporally segment fine-grained human actions throughout a
video is crucial for robotics, surveillance, education, and beyond. Typical approaches …

The potential for agents, whether embodied or software, to learn by observing other agents
performing procedures involving objects and actions is rich. Current research on automatic …

We study supervised action segmentation whose goal is to predict framewise action labels
of a video. To capture temporal dependencies over long horizons prior works either improve …

We propose an effective framework for the temporal action segmentation task, namely an
Action Segment Refinement Framework (ASRF). Our model architecture consists of a long …

Modeling long-term context in videos is crucial for many fine-grained tasks including
temporal action segmentation. An interesting question that is still open is how much long …