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 24012425 of 2759 papers

TitleStatusHype
Skeleton-based Action Recognition Using LSTM and CNN0
Learning Human Pose Models from Synthesized Data for Robust RGB-D Action Recognition0
Zero-Shot Action Recognition With Error-Correcting Output Codes0
Modeling Sub-Event Dynamics in First-Person Action Recognition0
Deep Sequential Context Networks for Action Prediction0
Multi-Task Clustering of Human Actions by Sharing Information0
Spatio-Temporal Vector of Locally Max Pooled Features for Action Recognition in Videos0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition0
Spatio-Temporal Naive-Bayes Nearest-Neighbor (ST-NBNN) for Skeleton-Based Action RecognitionCode0
Temporal Residual Networks for Dynamic Scene RecognitionCode0
Joint Discriminative Bayesian Dictionary and Classifier Learning0
Binary Coding for Partial Action Analysis With Limited Observation Ratios0
Alternative Semantic Representations for Zero-Shot Human Action Recognition0
Recurrent Residual Learning for Action Recognition0
Skeleton-Based Action Recognition Using Spatio-Temporal LSTM Network with Trust Gates0
Detekcja upadku i wybranych akcji na sekwencjach obrazów cyfrowych0
Learning without Prejudice: Avoiding Bias in Webly-Supervised Action Recognition0
Joint Max Margin and Semantic Features for Continuous Event Detection in Complex Scenes0
Learning to Learn from Noisy Web Videos0
Concurrence-Aware Long Short-Term Sub-Memories for Person-Person Action Recognition0
Deep manifold-to-manifold transforming network for action recognition0
Continuous Video to Simple Signals for Swimming Stroke Detection with Convolutional Neural Networks0
Sequence Summarization Using Order-constrained Kernelized Feature Subspaces0
Ridiculously Fast Shot Boundary Detection with Fully Convolutional Neural NetworksCode0
Two-Stream 3D Convolutional Neural Network for Skeleton-Based 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