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.
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics.
Ranked #7 on Action Segmentation on GTEA
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.
Ranked #2 on Action Segmentation on 50 Salads
Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors.
Ranked #5 on Action Segmentation on 50 Salads
In this work, we address the task of weakly-supervised human action segmentation in long, untrimmed videos.