Action Segmentation is a challenging problem in high-level video understanding. In its simplest form, Action Segmentation aims to segment a temporally untrimmed video by time and label each segmented part with one of pre-defined action labels. The results of Action Segmentation can be further used as input to various applications, such as video-to-text and action localization.
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The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond.
Despite the recent progress of fully-supervised action segmentation techniques, the performance is still not fully satisfactory.
Ranked #1 on Action Segmentation on 50 Salads
Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics.
Ranked #4 on Action Segmentation on Breakfast
This paper is about labeling video frames with action classes under weak supervision in training, where we have access to a temporal ordering of actions, but their start and end frames in training videos are unknown.
We present an approach for weakly supervised learning of human actions.
To address these problems, we present a new boundary-aware cascade network by introducing two novel components.
In this work, we address the task of weakly-supervised human action segmentation in long, untrimmed videos.
Action detection and temporal segmentation of actions in videos are topics of increasing interest.