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 951975 of 2759 papers

TitleStatusHype
Actions ~ TransformationsCode0
First and Second Order Dynamics in a Hierarchical SOM system for Action RecognitionCode0
An Animation-based Augmentation Approach for Action Recognition from Discontinuous VideoCode0
First-Person Hand Action Benchmark with RGB-D Videos and 3D Hand Pose AnnotationsCode0
Human activity recognition from skeleton posesCode0
A Recurrent Transformer Network for Novel View Action SynthesisCode0
Cross and Learn: Cross-Modal Self-SupervisionCode0
Are current long-term video understanding datasets long-term?Code0
Human Action Recognition by Representing 3D Skeletons as Points in a Lie GroupCode0
Counterfactual Gradients-based Quantification of Prediction Trust in Neural NetworksCode0
HomE: Homography-Equivariant Video Representation LearningCode0
Temporal Unet: Sample Level Human Action Recognition using WiFiCode0
HopaDIFF: Holistic-Partial Aware Fourier Conditioned Diffusion for Referring Human Action Segmentation in Multi-Person ScenariosCode0
Action Selection Learning for Multi-label Multi-view Action RecognitionCode0
CoTeRe-Net: Discovering Collaborative Ternary Relations in VideosCode0
H-MoRe: Learning Human-centric Motion Representation for Action AnalysisCode0
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Flow Snapshot Neurons in Action: Deep Neural Networks Generalize to Biological Motion PerceptionCode0
HPERL: 3D Human Pose Estimation from RGB and LiDARCode0
Hierarchical growing grid networks for skeleton based action recognitionCode0
Actional-Structural Graph Convolutional Networks for Skeleton-based Action RecognitionCode0
Convolutional Two-Stream Network Fusion for Video Action RecognitionCode0
Appearance-and-Relation Networks for Video ClassificationCode0
Hierarchical Explanations for Video Action RecognitionCode0
ConvGRU in Fine-grained Pitching Action Recognition for Action Outcome PredictionCode0
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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