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

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Showing 1251–1300 of 2759 papers

TitleStatusHype
Evolving Skeletons: Motion Dynamics in Action Recognition—0
Evolving Losses for Unsupervised Video Representation Learning—0
CA^2ST: Cross-Attention in Audio, Space, and Time for Holistic Video Recognition—0
Align before Adapt: Leveraging Entity-to-Region Alignments for Generalizable Video Action Recognition—0
Evolution-Preserving Dense Trajectory Descriptors—0
Bypass Enhancement RGB Stream Model for Pedestrian Action Recognition of Autonomous Vehicles—0
Event Transformer+. A multi-purpose solution for efficient event data processing—0
EventTransAct: A video transformer-based framework for Event-camera based action recognition—0
Bullying10K: A Large-Scale Neuromorphic Dataset towards Privacy-Preserving Bullying Recognition—0
Building Neural Networks on Matrix Manifolds: A Gyrovector Space Approach—0
ALGO: Object-Grounded Visual Commonsense Reasoning for Open-World Egocentric Action Recognition—0
Action recognition from depth maps using deep convolutional neural networks—0
3D Convolutional with Attention for Action Recognition—0
Event Masked Autoencoder: Point-wise Action Recognition with Event-Based Cameras—0
EventCrab: Harnessing Frame and Point Synergy for Event-based Action Recognition and Beyond—0
ALBAR: Adversarial Learning approach to mitigate Biases in Action Recognition—0
Event-based Timestamp Image Encoding Network for Human Action Recognition and Anticipation—0
Event-based Action Recognition Using Timestamp Image Encoding Network—0
Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems—0
Evaluation of Color STIPs for Human Action Recognition—0
Bubblenet: A Disperse Recurrent Structure To Recognize Activities—0
A Latent Clothing Attribute Approach for Human Pose Estimation—0
Evaluating Transformers for Lightweight Action Recognition—0
Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens—0
A Large-scale Varying-view RGB-D Action Dataset for Arbitrary-view Human Action Recognition—0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge: Mixed Sequences Prediction—0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report—0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2021: Team M3EM Technical Report—0
Bridging the gap between Human Action Recognition and Online Action Detection—0
Action Recognition for American Sign Language—0
Accuracy and Performance Comparison of Video Action Recognition Approaches—0
Bregman Divergences for Infinite Dimensional Covariance Matrices—0
Ensembles of Deep Neural Networks for Action Recognition in Still Images—0
Brain-inspired Computational Modeling of Action Recognition with Recurrent Spiking Neural Networks Equipped with Reinforcement Delay Learning—0
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition—0
BQN: Busy-Quiet Net Enabled by Motion Band-Pass Module for Action Recognition—0
Boundary-Aware Proposal Generation Method for Temporal Action Localization—0
A Large-Scale Robustness Analysis of Video Action Recognition Models—0
Action recognition by learning pose representations—0
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis—0
Enhancing Video Transformers for Action Understanding with VLM-aided Training—0
Bootstrapped Representation Learning for Skeleton-Based Action Recognition—0
Boosting Video Representation Learning with Multi-Faceted Integration—0
A Large-Scale Re-identification Analysis in Sporting Scenarios: the Betrayal of Reaching a Critical Point—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
Enhanced Spatiotemporal Prediction Using Physical-guided And Frequency-enhanced Recurrent Neural Networks—0
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
A Key Volume Mining Deep Framework for Action Recognition—0
Action Recognition by Hierarchical Sequence Summarization—0
A Cause and Effect Analysis of Motion Trajectories for Modeling Actions—0
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All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec23-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified