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 …

Understanding human behavior and activity facilitates advancement of numerous real-world
applications, and is critical for video analysis. Despite the progress of action recognition …

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 …

With the success of deep learning in classifying short trimmed videos, more attention has
been focused on temporally segmenting and classifying activities in long untrimmed videos …

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 …

Most activity localization methods in the literature suffer from the burden of frame-wise
annotation requirement. Learning from weak labels may be a potential solution towards …

Current action recognition methods heavily rely on trimmed videos for model training.
However, it is expensive and time-consuming to acquire a large-scale trimmed video …

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 …

In this paper we investigate learning visual models for the steps of ordinary tasks using weak
supervision via instructional narrations and an ordered list of steps instead of strong …