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 11011125 of 2759 papers

TitleStatusHype
On Modality Bias Recognition and ReductionCode0
Motion-driven Visual Tempo Learning for Video-based Action RecognitionCode1
Delving Deep into One-Shot Skeleton-based Action Recognition with Diverse OcclusionsCode1
Skeleton Sequence and RGB Frame Based Multi-Modality Feature Fusion Network for Action Recognition0
Student Dangerous Behavior Detection in SchoolCode1
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study0
Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision0
ActionFormer: Localizing Moments of Actions with TransformersCode2
HAA4D: Few-Shot Human Atomic Action Recognition via 3D Spatio-Temporal Skeletal Alignment0
HAKE: A Knowledge Engine Foundation for Human Activity UnderstandingCode2
Source-Free Progressive Graph Learning for Open-Set Domain AdaptationCode1
Joint-bone Fusion Graph Convolutional Network for Semi-supervised Skeleton Action Recognition0
CZU-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and 10 wearable inertial sensorsCode1
Bootstrapped Representation Learning for Skeleton-Based Action Recognition0
Towards To-a-T Spatio-Temporal Focus for Skeleton-Based Action Recognition0
ADG-Pose: Automated Dataset Generation for Real-World Human Pose EstimationCode0
Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Dataset0
Head and eye egocentric gesture recognition for human-robot interaction using eyewear cameras0
Capturing Temporal Information in a Single Frame: Channel Sampling Strategies for Action RecognitionCode0
Semantically Video Coding: Instill Static-Dynamic Clues into Structured Bitstream for AI Tasks0
Omnivore: A Single Model for Many Visual ModalitiesCode2
MeMViT: Memory-Augmented Multiscale Vision Transformer for Efficient Long-Term Video RecognitionCode1
Self-supervised Video Representation Learning with Cascade Positive RetrievalCode0
Action Keypoint Network for Efficient Video Recognition0
Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video SurveillanceCode0
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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