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 226250 of 2759 papers

TitleStatusHype
EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal TokensCode1
Look More but Care Less in Video RecognitionCode1
Hypergraph Transformer for Skeleton-based Action RecognitionCode1
Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive SurveyCode1
A Unified Multimodal De- and Re-coupling Framework for RGB-D Motion RecognitionCode1
Bringing Online Egocentric Action Recognition into the wildCode1
GliTr: Glimpse Transformers with Spatiotemporal Consistency for Online Action PredictionCode1
Holistic Interaction Transformer Network for Action DetectionCode1
An Action Is Worth Multiple Words: Handling Ambiguity in Action RecognitionCode1
Towards a Unified View on Visual Parameter-Efficient Transfer LearningCode1
Spiking Neural Networks for event-based action recognition: A new task to understand their advantageCode1
Learning State-Aware Visual Representations from Audible InteractionsCode1
Multi-dataset Training of Transformers for Robust Action RecognitionCode1
Weakly Supervised Two-Stage Training Scheme for Deep Video Fight Detection ModelCode1
Mitigating Representation Bias in Action Recognition: Algorithms and BenchmarksCode1
Hierarchical Temporal Transformer for 3D Hand Pose Estimation and Action Recognition from Egocentric RGB VideosCode1
ViA: View-invariant Skeleton Action Representation Learning via Motion RetargetingCode1
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual DistillationCode1
Lane Change Classification and Prediction with Action Recognition NetworksCode1
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
Net2Brain: A Toolbox to compare artificial vision models with human brain responsesCode1
Spatial Temporal Graph Attention Network for Skeleton-Based Action RecognitionCode1
Unsupervised Video Domain Adaptation for Action Recognition: A Disentanglement PerspectiveCode1
PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action RecognitionCode1
Generative Action Description Prompts for Skeleton-based Action RecognitionCode1
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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
4InternVideoTop-1 Accuracy77.2Unverified
5DejaVidTop-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
3OmniVec3-fold Accuracy99.6Unverified
4VideoMAE V2-g3-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
9PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified