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 12511275 of 2759 papers

TitleStatusHype
Temporal Alignment Prediction for Supervised Representation Learning and Few-Shot Sequence ClassificationCode1
Fundamental Limits of Transfer Learning in Binary Classifications0
Self-Supervised Learning of Motion-Informed Latents0
Cross-Stage Transformer for Video Learning0
UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation LearningCode1
Vi-MIX FOR SELF-SUPERVISED VIDEO REPRESENTATION0
Information Elevation Network for Fast Online Action Detection0
Fusion-GCN: Multimodal Action Recognition using Graph Convolutional NetworksCode1
Self-Supervised Video Representation Learning by Video Incoherence Detection0
Long Short View Feature Decomposition via Contrastive Video Representation Learning0
Unsupervised View-Invariant Human Posture Representation0
ActionCLIP: A New Paradigm for Video Action RecognitionCode1
Adversarial Bone Length Attack on Action Recognition0
Egocentric View Hand Action Recognition by Leveraging Hand Surface and Hand Grasp Type0
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition0
Improving Phenotype Prediction using Long-Range Spatio-Temporal Dynamics of Functional ConnectivityCode1
Efficient Action Recognition Using Confidence Distillation0
Video Pose Distillation for Few-Shot, Fine-Grained Sports Action RecognitionCode1
Conditional Extreme Value Theory for Open Set Video Domain AdaptationCode0
LIGAR: Lightweight General-purpose Action Recognition0
Learning Cross-modal Contrastive Features for Video Domain Adaptation0
Shifted Chunk Transformer for Spatio-Temporal Representational Learning0
BiaSwap: Removing dataset bias with bias-tailored swapping augmentation0
Few Shot Activity Recognition Using Variational Inference0
MM-ViT: Multi-Modal Video Transformer for Compressed Video 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