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 18761900 of 2759 papers

TitleStatusHype
Action Recognition Using Volumetric Motion RepresentationsCode0
SMART: Skeletal Motion Action Recognition aTtack0
Multi-attention Networks for Temporal Localization of Video-level LabelsCode0
RWF-2000: An Open Large Scale Video Database for Violence DetectionCode0
Deep-Aligned Convolutional Neural Network for Skeleton-based Action Recognition and Segmentation0
Guided Weak Supervision for Action Recognition with Scarce Data to Assess Skills of Children with AutismCode0
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural SearchingCode0
Action Recognition Using Supervised Spiking Neural Networks0
A Spectral Nonlocal Block for Neural Networks0
Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video UnderstandingCode0
Comprehensive Video Understanding: Video summarization with content-based video recommender design0
Skip-Clip: Self-Supervised Spatiotemporal Representation Learning by Future Clip Order Ranking0
Spatial Residual Layer and Dense Connection Block Enhanced Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition0
Human Action Recognition Using Deep Multilevel Multimodal (M2) Fusion of Depth and Inertial SensorsCode0
Controllable Attention for Structured Layered Video Decomposition0
Spatiotemporal Tile-based Attention-guided LSTMs for Traffic Video PredictionCode0
Human Action Recognition in Drone Videos using a Few Aerial Training Examples0
Predictive Coding Networks Meet Action Recognition0
Volterra Neural Networks (VNNs)Code1
MeteorNet: Deep Learning on Dynamic 3D Point Cloud SequencesCode1
Looking Ahead: Anticipating Pedestrians Crossing with Future Frames Prediction0
Making Third Person Techniques Recognize First-Person Actions in Egocentric VideosCode0
Adaptive and Iteratively Improving Recurrent Lateral ConnectionsCode0
Human Action Recognition with Multi-Laplacian Graph Convolutional Networks0
Seeing and Hearing Egocentric Actions: How Much Can We Learn?Code0
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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