Action Segmentation
43 papers with code • 4 benchmarks • 10 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
Breakfast
JIGSAWS
GTEA
50 Salads
Watch-n-Patch
TUM Kitchen
Assembly101
Most implemented papers
Temporal Convolutional Networks for Action Segmentation and Detection
coderSkyChen/Action_Recognition_Zoo
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CVPR 2017
The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond.
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
antoine77340/MIL-NCE_HowTo100M
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CVPR 2020
Annotating videos is cumbersome, expensive and not scalable.
Alleviating Over-segmentation Errors by Detecting Action Boundaries
yiskw713/asrf
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Our model architecture consists of a long-term feature extractor and two branches: the Action Segmentation Branch (ASB) and the Boundary Regression Branch (BRB).
Temporally-Weighted Hierarchical Clustering for Unsupervised Action Segmentation
Action segmentation refers to inferring boundaries of semantically consistent visual concepts in videos and is an important requirement for many video understanding tasks.
VideoCLIP: Contrastive Pre-training for Zero-shot Video-Text Understanding
pytorch/fairseq
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EMNLP 2021
We present VideoCLIP, a contrastive approach to pre-train a unified model for zero-shot video and text understanding, without using any labels on downstream tasks.
Temporal Convolutional Networks: A Unified Approach to Action Segmentation
The dominant paradigm for video-based action segmentation is composed of two steps: first, for each frame, compute low-level features using Dense Trajectories or a Convolutional Neural Network that encode spatiotemporal information locally, and second, input these features into a classifier that captures high-level temporal relationships, such as a Recurrent Neural Network (RNN).
Weakly Supervised Action Learning with RNN based Fine-to-coarse Modeling
We present an approach for weakly supervised learning of human actions.
Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints
alexanderrichard/action-sets
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CVPR 2018
Action detection and temporal segmentation of actions in videos are topics of increasing interest.
007: Democratically Finding The Cause of Packet Drops
Network failures continue to plague datacenter operators as their symptoms may not have direct correlation with where or why they occur.
Temporal Human Action Segmentation via Dynamic Clustering
We present an effective dynamic clustering algorithm for the task of temporal human action segmentation, which has comprehensive applications such as robotics, motion analysis, and patient monitoring.
