Action Classification
227 papers with code • 24 benchmarks • 30 datasets
Image source: The Kinetics Human Action Video Dataset
Benchmarks
These leaderboards are used to track progress in Action Classification
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Action Classification models and implementationsDatasets
UCF101
Kinetics
HMDB51
ActivityNet
Kinetics 400
Charades
THUMOS14
Something-Something V2
YouCook2
Kinetics-600
Most implemented papers
High Quality Monocular Depth Estimation via Transfer Learning
Accurate depth estimation from images is a fundamental task in many applications including scene understanding and reconstruction.
Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset
open-mmlab/mmaction2
•
•
CVPR 2017
The paucity of videos in current action classification datasets (UCF-101 and HMDB-51) has made it difficult to identify good video architectures, as most methods obtain similar performance on existing small-scale benchmarks.
Non-local Neural Networks
facebookresearch/video-nonlocal-net
•
•
CVPR 2018
Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time.
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
facebookresearch/OctConv
•
•
ICCV 2019
Similarly, the output feature maps of a convolution layer can also be seen as a mixture of information at different frequencies.
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.
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.
Swin Transformer V2: Scaling Up Capacity and Resolution
microsoft/Swin-Transformer
•
•
CVPR 2022
Three main techniques are proposed: 1) a residual-post-norm method combined with cosine attention to improve training stability; 2) A log-spaced continuous position bias method to effectively transfer models pre-trained using low-resolution images to downstream tasks with high-resolution inputs; 3) A self-supervised pre-training method, SimMIM, to reduce the needs of vast labeled images.
SlowFast Networks for Video Recognition
facebookresearch/SlowFast
•
•
ICCV 2019
We present SlowFast networks for video recognition.
Video Swin Transformer
SwinTransformer/Video-Swin-Transformer
•
•
CVPR 2022
The vision community is witnessing a modeling shift from CNNs to Transformers, where pure Transformer architectures have attained top accuracy on the major video recognition benchmarks.
TSM: Temporal Shift Module for Efficient Video Understanding
MIT-HAN-LAB/temporal-shift-module
•
•
ICCV 2019
The explosive growth in video streaming gives rise to challenges on performing video understanding at high accuracy and low computation cost.
