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 25012525 of 2759 papers

TitleStatusHype
Real-time Human Pose Estimation from Video with Convolutional Neural Networks0
Pose from Action: Unsupervised Learning of Pose Features based on Motion0
Multi-region two-stream R-CNN for action detection0
Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups0
Sequential Deep Trajectory Descriptor for Action Recognition with Three-stream CNN0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories0
Image and Video Mining through Online Learning0
Making a Case for Learning Motion Representations with Phase0
Human Action Recognition without Human0
Spatio-temporal Aware Non-negative Component Representation for Action Recognition0
Sympathy for the Details: Dense Trajectories and Hybrid Classification Architectures for Action Recognition0
We Can "See" You via Wi-Fi - WiFi Action Recognition via Vision-based Methods0
Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition0
Discriminatively Trained Latent Ordinal Model for Video Classification0
Multiview Cauchy Estimator Feature Embedding for Depth and Inertial Sensor-Based Human Action Recognition0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification0
Temporal Segment Networks: Towards Good Practices for Deep Action RecognitionCode2
A Survey of Visual Analysis of Human Motion and Its Applications0
Dynamic Probabilistic Network Based Human Action Recognition0
Emotion-Based Crowd Representation for Abnormality Detection0
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition0
Kinematic-Layout-aware Random Forests for Depth-based Action Recognition0
Hierarchical Attention Network for Action Recognition in Videos0
Multi-Camera Action Dataset for Cross-Camera Action Recognition Benchmarking0
Annotation Methodologies for Vision and Language Dataset Creation0
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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