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 10511075 of 2759 papers

TitleStatusHype
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision SensingCode0
Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling0
Composable Augmentation Encoding for Video Representation Learning0
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models0
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition0
Complex Video Action Reasoning via Learnable Markov Logic Network0
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data0
Generalized Rank Pooling for Activity Recognition0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression0
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition0
Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method0
Ani-GIFs: A benchmark dataset for domain generalization of action recognition from GIFs0
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion0
GCF-Net: Gated Clip Fusion Network for Video Action Recognition0
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark0
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions0
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition0
Future Aspects in Human Action Recognition: Exploring Emerging Techniques and Ethical Influences0
A New Representation of Skeleton Sequences for 3D Action Recognition0
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision0
ActAR: Actor-Driven Pose Embeddings for Video Action Recognition0
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification0
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices0
C3T: Cross-modal Transfer Through Time for Sensor-based Human Activity Recognition0
Fusing multiple features for depth-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
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