SOTAVerified

Action Recognition

Action Recognition is a computer vision task that involves recognizing human actions in videos or images. The goal is to classify and categorize the actions being performed in the video or image into a predefined set of action classes.

In the video domain, it is an open question whether training an action classification network on a sufficiently large dataset, will give a similar boost in performance when applied to a different temporal task or dataset. The challenges of building video datasets has meant that most popular benchmarks for action recognition are small, having on the order of 10k videos.

Please note some benchmarks may be located in the Action Classification or Video Classification tasks, e.g. Kinetics-400.

Papers

Showing 12761300 of 2759 papers

TitleStatusHype
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed Sequences Prediction0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report0
Bridging the gap between Human Action Recognition and Online Action Detection0
Action Recognition for American Sign Language0
Accuracy and Performance Comparison of Video Action Recognition Approaches0
Bregman Divergences for Infinite Dimensional Covariance Matrices0
Ensembles of Deep Neural Networks for Action Recognition in Still Images0
Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning0
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition0
BQN: Busy-Quiet Net Enabled by Motion Band-Pass Module for Action Recognition0
Boundary-Aware Proposal Generation Method for Temporal Action Localization0
A Large-Scale Robustness Analysis of Video Action Recognition Models0
Action recognition by learning pose representations0
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis0
Enhancing Video Transformers for Action Understanding with VLM-aided Training0
Bootstrapped Representation Learning for Skeleton-Based Action Recognition0
Boosting Video Representation Learning with Multi-Faceted Integration0
A Large-Scale Re-identification Analysis in Sporting Scenarios: the Betrayal of Reaching a Critical Point0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation0
Enhanced Spatiotemporal Prediction Using Physical-guided And Frequency-enhanced Recurrent Neural Networks0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space0
A Key Volume Mining Deep Framework for Action Recognition0
Action Recognition by Hierarchical Sequence Summarization0
A Cause and Effect Analysis of Motion Trajectories for Modeling Actions0
Show:102550
← PrevPage 52 of 111Next →

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3Unverified
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-1 Accuracy77.2Unverified
6InternVideo2-1BTop-1 Accuracy77.1Unverified
7VideoMAE V2-gTop-1 Accuracy77Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5Unverified
10TubeViT-LTop-1 Accuracy76.1Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7Unverified
2OmniVec23-fold Accuracy99.6Unverified
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified