Action Segmentation
99 papers with code • 9 benchmarks • 18 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Action Segmentation models and implementationsDatasets
Breakfast
GTEA
JIGSAWS
COIN
Assembly101
Watch-n-Patch
EgoExoLearn
EgoProceL
Subtasks
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.
End-to-End Learning of Visual Representations from Uncurated Instructional Videos
antoine77340/MIL-NCE_HowTo100M
•
•
CVPR 2020
Annotating videos is cumbersome, expensive and not scalable.
Temporal Action Segmentation: An Analysis of Modern Techniques
Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes.
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality Assessment
laurenok24/nsaqa
•
•
We found that domain experts prefer our system and find it more informative than purely neural approaches to AQA in diving.
MS-TCN: Multi-Stage Temporal Convolutional Network for Action Segmentation
yabufarha/ms-tcn
•
•
CVPR 2019
Temporally locating and classifying action segments in long untrimmed videos is of particular interest to many applications like surveillance and robotics.
Unsupervised learning of action classes with continuous temporal embedding
annusha/unsup_temp_embed
•
•
CVPR 2019
The task of temporally detecting and segmenting actions in untrimmed videos has seen an increased attention recently.
UniVL: A Unified Video and Language Pre-Training Model for Multimodal Understanding and Generation
microsoft/UniVL
•
•
However, most of the existing multimodal models are pre-trained for understanding tasks, leading to a pretrain-finetune discrepancy for generation tasks.
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
Our search scheme exploits both global search to find the coarse combinations and local search to get the refined receptive field combinations further.
