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 10261050 of 2759 papers

TitleStatusHype
Hierarchical Explanations for Video Action RecognitionCode0
Action Recognition with Dynamic Image NetworksCode0
HA-ViD: A Human Assembly Video Dataset for Comprehensive Assembly Knowledge UnderstandingCode0
GPRAR: Graph Convolutional Network based Pose Reconstruction and Action Recognition for Human Trajectory PredictionCode0
Hierarchical growing grid networks for skeleton based action recognitionCode0
HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of ActionsCode0
HalluciNet-ing Spatiotemporal Representations Using a 2D-CNNCode0
HaltingVT: Adaptive Token Halting Transformer for Efficient Video RecognitionCode0
GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation LearningCode0
Handwashing Action Detection System for an Autonomous Social RobotCode0
Growing a Brain with Sparsity-Inducing Generation for Continual LearningCode0
Guided Weak Supervision for Action Recognition with Scarce Data to Assess Skills of Children with AutismCode0
Global Semantic Descriptors for Zero-Shot Action RecognitionCode0
Grouped Spatial-Temporal Aggregation for Efficient Action RecognitionCode0
Group Ensemble: Learning an Ensemble of ConvNets in a single ConvNetCode0
HACS: Human Action Clips and Segments Dataset for Recognition and Temporal LocalizationCode0
Glimpse Clouds: Human Activity Recognition from Unstructured Feature PointsCode0
Compressed Video Action RecognitionCode0
GLAD: Global-Local View Alignment and Background Debiasing for Unsupervised Video Domain Adaptation with Large Domain GapCode0
Group Activity Recognition Using Joint Learning of Individual Action Recognition and People GroupingCode0
SoccerDB: A Large-Scale Database for Comprehensive Video UnderstandingCode0
2D/3D Pose Estimation and Action Recognition using Multitask Deep LearningCode0
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision SensingCode0
HARFLOW3D: A Latency-Oriented 3D-CNN Accelerator Toolflow for HAR on FPGA DevicesCode0
High-Performance Inference Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
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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