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 701725 of 2759 papers

TitleStatusHype
Bayesian Hierarchical Dynamic Model for Human Action RecognitionCode0
Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action DetectionCode0
Memory Attention Networks for Skeleton-based Action RecognitionCode0
MetaVD: A Meta Video Dataset for enhancing human action recognition datasetsCode0
VideoDG: Generalizing Temporal Relations in Videos to Novel DomainsCode0
Balanced Representation Learning for Long-tailed Skeleton-based Action RecognitionCode0
A Bag-of-Words Equivalent Recurrent Neural Network for Action RecognitionCode0
MD-BERT: Action Recognition in Dark Videos via Dynamic Multi-Stream Fusion and Temporal ModelingCode0
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual LearningCode0
Back to the Future: Cycle Encoding Prediction for Self-supervised Contrastive Video Representation LearningCode0
MARS: Motion-Augmented RGB Stream for Action RecognitionCode0
3D CNNs on Distance Matrices for Human Action RecognitionCode0
MARINE: A Computer Vision Model for Detecting Rare Predator-Prey Interactions in Animal VideosCode0
Adversarial Augmentation Training Makes Action Recognition Models More Robust to Realistic Video Distribution ShiftsCode0
MFAS: Multimodal Fusion Architecture SearchCode0
Making Third Person Techniques Recognize First-Person Actions in Egocentric VideosCode0
A Video-based End-to-end Pipeline for Non-nutritive Sucking Action Recognition and Segmentation in Young InfantsCode0
Advancing Compressed Video Action Recognition through Progressive Knowledge DistillationCode0
LSTA: Long Short-Term Attention for Egocentric Action RecognitionCode0
A Variational Time Series Feature Extractor for Action PredictionCode0
Low-light Environment Neural SurveillanceCode0
DMCL: Distillation Multiple Choice Learning for Multimodal Action RecognitionCode0
A^2-Nets: Double Attention NetworksCode0
LSFB-CONT and LSFB-ISOL: Two New Datasets for Vision-Based Sign Language RecognitionCode0
LSTA-Net: Long short-term Spatio-Temporal Aggregation Network for Skeleton-based Action RecognitionCode0
Show:102550
← PrevPage 29 of 111Next →

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