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 301350 of 2759 papers

TitleStatusHype
ImageNet-21K Pretraining for the MassesCode1
Implicit Temporal Modeling with Learnable Alignment for Video RecognitionCode1
3DInAction: Understanding Human Actions in 3D Point CloudsCode1
InfoGCN++: Learning Representation by Predicting the Future for Online Human Skeleton-based Action RecognitionCode1
Concatenated Masked Autoencoders as Spatial-Temporal LearnerCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
Volterra Neural Networks (VNNs)Code1
Conquering the cnn over-parameterization dilemma: A volterra filtering approach for action recognitionCode1
Actor-Context-Actor Relation Network for Spatio-Temporal Action LocalizationCode1
Constructing Stronger and Faster Baselines for Skeleton-based Action RecognitionCode1
HDBN: A Novel Hybrid Dual-branch Network for Robust Skeleton-based Action RecognitionCode1
Hierarchically Decomposed Graph Convolutional Networks for Skeleton-Based Action RecognitionCode1
Counterfactual Debiasing Inference for Compositional Action RecognitionCode1
Interactive Fusion of Multi-level Features for Compositional Activity RecognitionCode1
Large Scale Holistic Video UnderstandingCode1
BEVT: BERT Pretraining of Video TransformersCode1
ConvNet Architecture Search for Spatiotemporal Feature LearningCode1
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical AggregationCode1
CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensorsCode1
Decoupling GCN with DropGraph Module for Skeleton-Based Action RecognitionCode1
Graph Convolution with Low-rank Learnable Local FiltersCode1
Keeping Your Eye on the Ball: Trajectory Attention in Video TransformersCode1
Group Contextualization for Video RecognitionCode1
Language Knowledge-Assisted Representation Learning for Skeleton-Based Action RecognitionCode1
Attention-Based Context Aware Reasoning for Situation RecognitionCode1
Generative Action Description Prompts for Skeleton-based Action RecognitionCode1
Benchmarking Micro-action Recognition: Dataset, Methods, and ApplicationsCode1
Decoupled Spatial-Temporal Attention Network for Skeleton-Based Action RecognitionCode1
Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal ModelingCode1
3DV: 3D Dynamic Voxel for Action Recognition in Depth VideoCode1
Action Genome: Actions as Composition of Spatio-temporal Scene GraphsCode1
DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action RecognitionCode1
Learning Multi-Granular Spatio-Temporal Graph Network for Skeleton-based Action RecognitionCode1
DDGCN: A Dynamic Directed Graph Convolutional Network for Action RecognitionCode1
Graph in Graph Neural NetworkCode1
BASAR:Black-box Attack on Skeletal Action RecognitionCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
Deep Multimodal Feature Encoding for Video OrderingCode1
Audio-Visual Instance Discrimination with Cross-Modal AgreementCode1
DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 PandemicCode1
Grad-CAM++: Improved Visual Explanations for Deep Convolutional NetworksCode1
Augmented Neural Fine-Tuning for Efficient Backdoor PurificationCode1
Anonymization for Skeleton Action RecognitionCode1
Depth Guided Adaptive Meta-Fusion Network for Few-shot Video RecognitionCode1
A Deeper Dive Into What Deep Spatiotemporal Networks Encode: Quantifying Static vs. Dynamic InformationCode1
Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D SpaceCode1
B2C-AFM: Bi-Directional Co-Temporal and Cross-Spatial Attention Fusion Model for Human Action RecognitionCode1
DEVIAS: Learning Disentangled Video Representations of Action and SceneCode1
A Dense-Sparse Complementary Network for Human Action Recognition based on RGB and Skeleton ModalitiesCode1
BABEL: Bodies, Action and Behavior with English LabelsCode1
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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
2OmniVec3-fold Accuracy99.6Unverified
3VideoMAE V2-g3-fold Accuracy99.6Unverified
4OmniVec23-fold Accuracy99.6Unverified
5BIKE3-fold Accuracy98.8Unverified
6SMART3-fold Accuracy98.64Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6Unverified
9OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6Unverified
10Text4Vis3-fold Accuracy98.2Unverified