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

Most implemented papers

Temporal Convolutional Networks for Action Segmentation and Detection

coderSkyChen/Action_Recognition_Zoo CVPR 2017

The ability to identify and temporally segment fine-grained human actions throughout a video is crucial for robotics, surveillance, education, and beyond.

Alleviating Over-segmentation Errors by Detecting Action Boundaries

yiskw713/asrf 14 Jul 2020

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

ssarfraz/FINCH-CLustering CVPR 2021

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 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

Around-30/Kaggle 29 Aug 2016

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).

Action Sets: Weakly Supervised Action Segmentation without Ordering Constraints

alexanderrichard/action-sets CVPR 2018

Action detection and temporal segmentation of actions in videos are topics of increasing interest.

007: Democratically Finding The Cause of Packet Drops

behnazak/Vigil-007SourceCode 20 Feb 2018

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

yz-cnsdqz/dynamic_clustering 15 Mar 2018

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.