Temporal Action Localization
308 papers with code • 10 benchmarks • 39 datasets
Temporal Action Localization aims to detect activities in the video stream and output beginning and end timestamps. It is closely related to Temporal Action Proposal Generation.
Benchmarks
These leaderboards are used to track progress in Temporal Action Localization
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Temporal Action Localization models and implementations
UCF101
Kinetics
HMDB51
ActivityNet
MPII
Charades
THUMOS14
KTH
JHMDB
EPIC-KITCHENS-100
Subtasks
Most implemented papers
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
yysijie/st-gcn
•
•
Dynamics of human body skeletons convey significant information for human action recognition.
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
adityac94/Grad_CAM_plus_plus
•
•
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems.
Temporal Segment Networks: Towards Good Practices for Deep Action Recognition
yjxiong/temporal-segment-networks
•
•
The other contribution is our study on a series of good practices in learning ConvNets on video data with the help of temporal segment network.
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
wzmsltw/BSN-boundary-sensitive-network.pytorch
•
•
ECCV 2018
Temporal action proposal generation is an important yet challenging problem, since temporal proposals with rich action content are indispensable for analysing real-world videos with long duration and high proportion irrelevant content.
A Closer Look at Spatiotemporal Convolutions for Action Recognition
facebookresearch/R2Plus1D
•
•
CVPR 2018
In this paper we discuss several forms of spatiotemporal convolutions for video analysis and study their effects on action recognition.
BMN: Boundary-Matching Network for Temporal Action Proposal Generation
PaddlePaddle/models
•
•
ICCV 2019
To address these difficulties, we introduce the Boundary-Matching (BM) mechanism to evaluate confidence scores of densely distributed proposals, which denote a proposal as a matching pair of starting and ending boundaries and combine all densely distributed BM pairs into the BM confidence map.
Unsupervised Learning of Video Representations using LSTMs
We further evaluate the representations by finetuning them for a supervised learning problem - human action recognition on the UCF-101 and HMDB-51 datasets.
Temporal Segment Networks for Action Recognition in Videos
yjxiong/temporal-segment-networks
•
•
Furthermore, based on the temporal segment networks, we won the video classification track at the ActivityNet challenge 2016 among 24 teams, which demonstrates the effectiveness of TSN and the proposed good practices.
Multivariate LSTM-FCNs for Time Series Classification
houshd/MLSTM-FCN
•
•
Over the past decade, multivariate time series classification has received great attention.
UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
To the best of our knowledge, UCF101 is currently the most challenging dataset of actions due to its large number of classes, large number of clips and also unconstrained nature of such clips.
