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 23762400 of 2759 papers

TitleStatusHype
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models0
Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling0
Generative Multi-View Human Action Recognition0
GeoDeformer: Geometric Deformable Transformer for Action Recognition0
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition0
Global Temporal Difference Network for Action Recognition0
Global Temporal Representation based CNNs for Infrared Action Recognition0
GoferBot: A Visual Guided Human-Robot Collaborative Assembly System0
Going Deeper into Action Recognition: A Survey0
Going Deeper into First-Person Activity Recognition0
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study0
GPT4Ego: Unleashing the Potential of Pre-trained Models for Zero-Shot Egocentric Action Recognition0
Gradient Boundary Histograms for Action Recognition0
Gradient Forward-Propagation for Large-Scale Temporal Video Modelling0
Gradient Frequency Modulation for Visually Explaining Video Understanding Models0
Gradient Weighted Superpixels for Interpretability in CNNs0
GradMix: Multi-source Transfer across Domains and Tasks0
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition0
Graph Based Skeleton Modeling for Human Activity Analysis0
Graph Convolutional Module for Temporal Action Localization in Videos0
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition0
Graph learning in robotics: a survey0
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation0
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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
4DejaVidTop-1 Accuracy77.2Unverified
5InternVideoTop-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
3VideoMAE V2-g3-fold Accuracy99.6Unverified
4OmniVec3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
10LGD-3D Two-stream3-fold Accuracy98.2Unverified