computer-vision-5
Action Segmentation
3260 papers • 126 benchmarks • 313 datasets
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. Source: TricorNet: A Hybrid Temporal Convolutional and Recurrent Network for Video Action Segmentation
(Image credit: Papersgraph)
Benchmarks
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Actions
Breakfast
Breakfast
50 Salads
50 Salads
GTEA
GTEA
Libraries
iUse these libraries to find action-segmentation-5 models and implementations
2 papers 29,273
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Most implemented papers
Temporal Convolutional Networks for Action Segmentation and Detection
R. Vidal, Gregory Hager, Colin S. Lea, A. Reiter, Michael D. Flynn•Tue Nov 15 2016
A class of temporal models that use a hierarchy of temporal convolutions to perform fine-grained action segmentation or detection, which are capable of capturing action compositions, segment durations, and long-range dependencies, and are over a magnitude faster to train than competing LSTM-based Recurrent Neural Networks.
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