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 action recognition and posture prediction aim to recognize and predict respectively
the action and postures of persons in videos. They are both active research topics in …

As humans, we understand events in the visual world contextually, performing multimodal
reasoning across time to make inferences about the past, present, and future. We introduce …

This paper introduces the pipeline to extend the largest dataset in egocentric vision, EPIC-
KITCHENS. The effort culminates in EPIC-KITCHENS-100, a collection of 100 hours, 20M …

We target at the task of weakly-supervised action localization (WSAL), where only video-
level action labels are available during model training. Despite the recent progress, existing …

Procedural activity understanding requires perceiving human actions in terms of a broader
task, where multiple keysteps are performed in sequence across a long video to reach a …

Difficulty in collecting and annotating large-scale video data raises a growing interest in
learning models which can recognize novel classes with only a few training examples. In …

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 address the problem of online action segmentation for egocentric procedural task
videos. While previous studies have mostly focused on offline action segmentation where …

Procedure learning involves identifying the key-steps and determining their logical order to
perform a task. Existing approaches commonly use third-person videos for learning the …