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 19011925 of 2759 papers

TitleStatusHype
Real-time monitoring of driver drowsiness on mobile platforms using 3D neural networks0
Multi-Stage HRNet: Multiple Stage High-Resolution Network for Human Pose Estimation0
Context-Gated ConvolutionCode0
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models0
Interaction Relational Network for Mutual Action RecognitionCode0
Cross-modal knowledge distillation for action recognition0
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision SensingCode0
Compressed Video Action Recognition with Refined Motion Vector0
Symbiotic Graph Neural Networks for 3D Skeleton-based Human Action Recognition and Motion Prediction0
Order-Preserving Wasserstein Discriminant Analysis0
View-LSTM: Novel-View Video Synthesis Through View Decomposition0
MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding0
Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition0
Generative Multi-View Human Action Recognition0
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos0
Drive&Act: A Multi-Modal Dataset for Fine-Grained Driver Behavior Recognition in Autonomous Vehicles0
Spatio-Temporal FAST 3D Convolutions for Human Action Recognition0
Grouped Spatial-Temporal Aggregation for Efficient Action RecognitionCode0
Discriminability Distillation in Group Representation Learning0
Learning deep representations for video-based intake gesture detectionCode0
Learning Coupled Spatial-temporal Attention for Skeleton-based Action Recognition0
Target-Specific Action Classification for Automated Assessment of Human Motor Behavior from Video0
Making the Invisible Visible: Action Recognition Through Walls and Occlusions0
Global Temporal Representation based CNNs for Infrared Action Recognition0
Transferable Feature Representation for Visible-to-Infrared Cross-Dataset Human Action Recognition0
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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