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 226250 of 2759 papers

TitleStatusHype
Event Stream based Human Action Recognition: A High-Definition Benchmark Dataset and AlgorithmsCode1
SHARP: Segmentation of Hands and Arms by Range using Pseudo-Depth for Enhanced Egocentric 3D Hand Pose Estimation and Action RecognitionCode1
Joint Temporal Pooling for Improving Skeleton-based Action Recognition0
Temporal Reversed Training for Spiking Neural Networks with Generalized Spatio-Temporal Representation0
Flatten: Video Action Recognition is an Image Classification task0
Towards Physical World Backdoor Attacks against Skeleton Action Recognition0
Action Recognition for Privacy-Preserving Ambient Assisted LivingCode0
EPAM-Net: An Efficient Pose-driven Attention-guided Multimodal Network for Video Action RecognitionCode1
Prototype Learning for Micro-gesture Classification0
From Recognition to Prediction: Leveraging Sequence Reasoning for Action AnticipationCode0
Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion0
MultiFuser: Multimodal Fusion Transformer for Enhanced Driver Action Recognition0
Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics0
Task-Adapter: Task-specific Adaptation of Image Models for Few-shot Action Recognition0
How Effective are Self-Supervised Models for Contact Identification in Videos0
Skeleton-Based Action Recognition with Spatial-Structural Graph ConvolutionCode0
Joint-Partition Group Attention for skeleton-based action recognitionCode0
Adversarial Robustness in RGB-Skeleton Action Recognition: Leveraging Attention Modality Reweighter0
MARINE: A Computer Vision Model for Detecting Rare Predator-Prey Interactions in Animal VideosCode0
Trajectory-aligned Space-time Tokens for Few-shot Action Recognition0
Harnessing Temporal Causality for Advanced Temporal Action DetectionCode3
C3T: Cross-modal Transfer Through Time for Sensor-based Human Activity Recognition0
SOAP: Enhancing Spatio-Temporal Relation and Motion Information Capturing for Few-Shot Action RecognitionCode0
Multi-Modality Co-Learning for Efficient Skeleton-based Action RecognitionCode1
A Comprehensive Review of Few-shot 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