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

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Showing 1651–1700 of 2759 papers

TitleStatusHype
The 3TConv: An Intrinsic Approach to Explainable 3D CNNs—0
Learning Visual Representation from Human Interactions—0
Vi2CLR: Video and Image for Visual Contrastive Learning of Representation—0
Efficient Action Recognition via Dynamic Knowledge Propagation—0
Self-Supervised 3D Skeleton Action Representation Learning With Motion Consistency and Continuity—0
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition—0
Temporal Difference Networks for Action Recognition—0
A Unified Framework to Analyze and Design the Nonlocal Blocks for Neural Networks—0
Normalized Human Pose Features for Human Action Video Alignment—0
Interactive Prototype Learning for Egocentric Action Recognition—0
3D Human motion anticipation and classification—0
2D or not 2D? Adaptive 3D Convolution Selection for Efficient Video Recognition—0
Action Recognition with Kernel-based Graph Convolutional Networks—0
Faster and Accurate Compressed Video Action Recognition Straight from the Frequency Domain—0
Human Action Recognition from Various Data Modalities: A Review—0
Anchor-Based Spatio-Temporal Attention 3D Convolutional Networks for Dynamic 3D Point Cloud Sequences—0
Recent Advances of Generic Object Detection with Deep Learning: A Review—0
SMART Frame Selection for Action Recognition—0
Smoothed Gaussian Mixture Models for Video Classification and Recommendation—0
Weakly-Supervised Action Localization and Action Recognition using Global-Local Attention of 3D CNN—0
Temporal Graph Modeling for Skeleton-based Action Recognition—0
NUTA: Non-uniform Temporal Aggregation for Action Recognition—0
Towards Improving Spatiotemporal Action Recognition in VideosCode0
GTA: Global Temporal Attention for Video Action Understanding—0
TDAF: Top-Down Attention Framework for Vision Tasks—0
Online Action RecognitionCode0
Developing Motion Code Embedding for Action Recognition in Videos—0
Multi Scale Temporal Graph Networks For Skeleton-based Action Recognition—0
Spatial-Temporal Alignment Network for Action Recognition and Detection—0
Recovering Trajectories of Unmarked Joints in 3D Human Actions Using Latent Space Optimization—0
SAFCAR: Structured Attention Fusion for Compositional Action Recognition—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Fine-grained activity recognition for assembly videos—0
Video Anomaly Detection by Estimating Likelihood of RepresentationsCode0
Learning View-Disentangled Human Pose Representation by Contrastive Cross-View Mutual Information Maximization—0
A compact sequence encoding scheme for online human activity recognition in HRI applications—0
A New Action Recognition Framework for Video Highlights Summarization in Sporting Events—0
Annotation-Efficient Untrimmed Video Action Recognition—0
Just One Moment: Structural Vulnerability of Deep Action Recognition against One Frame Attack—0
Group-Skeleton-Based Human Action Recognition in Complex Events—0
Depth-Aware Action Recognition: Pose-Motion Encoding through Temporal Heatmaps—0
Recent Progress in Appearance-based Action Recognition—0
KShapeNet: Riemannian network on Kendall shape space for Skeleton based Action Recognition—0
Independent Sign Language Recognition with 3D Body, Hands, and Face Reconstruction—0
A3D: Adaptive 3D Networks for Video Action Recognition—0
Hierarchically Decoupled Spatial-Temporal Contrast for Self-supervised Video Representation Learning—0
Modular Action Concept Grounding in Semantic Video Prediction—0
Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition—0
DARE: AI-based Diver Action Recognition System using Multi-Channel CNNs for AUV Supervision—0
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition—0
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All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec23-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified