Action Segmentation
72 papers with code • 9 benchmarks • 16 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
Benchmarks
These leaderboards are used to track progress in Action Segmentation
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Libraries
Use these libraries to find Action Segmentation models and implementationsDatasets
Breakfast
GTEA
JIGSAWS
COIN
Assembly101
Watch-n-Patch
TUM Kitchen
Subtasks
Latest papers with no code
O-TALC: Steps Towards Combating Oversegmentation within Online Action Segmentation
In order to facilitate online action segmentation on a stream of incoming video data, we introduce two methods for improved training and inference of backbone action recognition models, allowing them to be deployed directly for online frame level classification.
Coherent Temporal Synthesis for Incremental Action Segmentation
Data replay is a successful incremental learning technique for images.
A Multimodal Handover Failure Detection Dataset and Baselines
To address this deficit, we present the multimodal Handover Failure Detection dataset, which consists of failures induced by the human participant, such as ignoring the robot or not releasing the object.
ADL4D: Towards A Contextually Rich Dataset for 4D Activities of Daily Living
However, existing datasets for 4D HOI (3D HOI over time) are limited to one subject inter- acting with one object only.
ViSTec: Video Modeling for Sports Technique Recognition and Tactical Analysis
The immense popularity of racket sports has fueled substantial demand in tactical analysis with broadcast videos.
Friends Across Time: Multi-Scale Action Segmentation Transformer for Surgical Phase Recognition
Current state-of-the-art methods use both spatial and temporal information to tackle the surgical phase recognition task.
Depth Over RGB: Automatic Evaluation of Open Surgery Skills Using Depth Camera
We focused on hand and tool detection, and action segmentation in suturing procedures.
Robotic Imitation of Human Actions
Imitation can allow us to quickly gain an understanding of a new task.
SFGANS Self-supervised Future Generator for human ActioN Segmentation
The ability to locate and classify action segments in long untrimmed video is of particular interest to many applications such as autonomous cars, robotics and healthcare applications.
SMC-NCA: Semantic-guided Multi-level Contrast for Semi-supervised Temporal Action Segmentation
However, learning the representation of each frame by unsupervised contrastive learning for action segmentation remains an open and challenging problem.
