Action Segmentation

55 papers with code • 9 benchmarks • 14 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


Use these libraries to find Action Segmentation models and implementations
2 papers

Most implemented papers

Temporal Convolutional Networks for Action Segmentation and Detection

colincsl/TemporalConvolutionalNetworks CVPR 2017

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

UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation

microsoft/UniVL 15 Feb 2020

However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks.

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).

Global2Local: Efficient Structure Search for Video Action Segmentation

ShangHua-Gao/G2L-search CVPR 2021

Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combination patterns further.

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.

RF-Next: Efficient Receptive Field Search for Convolutional Neural Networks

ShangHua-Gao/RFNext 14 Jun 2022

Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further.

Unified Fully and Timestamp Supervised Temporal Action Segmentation via Sequence to Sequence Translation

boschresearch/uvast 1 Sep 2022

This paper introduces a unified framework for video action segmentation via sequence to sequence (seq2seq) translation in a fully and timestamp supervised setup.

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).