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 376400 of 2759 papers

TitleStatusHype
vid-TLDR: Training Free Token merging for Light-weight Video TransformerCode2
Hierarchical NeuroSymbolic Approach for Comprehensive and Explainable Action Quality AssessmentCode2
Benchmarking Badminton Action Recognition with a New Fine-Grained Dataset0
ExACT: Language-guided Conceptual Reasoning and Uncertainty Estimation for Event-based Action Recognition and MoreCode1
Selective, Interpretable, and Motion Consistent Privacy Attribute Obfuscation for Action Recognition0
Multi-View Video-Based Learning: Leveraging Weak Labels for Frame-Level Perception0
A Survey of IMU Based Cross-Modal Transfer Learning in Human Activity Recognition0
A Lie Group Approach to Riemannian Batch NormalizationCode1
CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner0
Skeleton-Based Human Action Recognition with Noisy LabelsCode0
On the Utility of 3D Hand Poses for Action RecognitionCode1
Leveraging Foundation Model Automatic Data Augmentation Strategies and Skeletal Points for Hands Action Recognition in Industrial Assembly Lines0
SkateFormer: Skeletal-Temporal Transformer for Human Action RecognitionCode2
EventRPG: Event Data Augmentation with Relevance Propagation GuidanceCode1
Real-Time Multimodal Cognitive Assistant for Emergency Medical ServicesCode1
Deep Learning Approaches for Human Action Recognition in Video Data0
Attention Prompt Tuning: Parameter-efficient Adaptation of Pre-trained Models for Spatiotemporal ModelingCode1
Transformer-based Fusion of 2D-pose and Spatio-temporal Embeddings for Distracted Driver Action Recognition0
Density-Guided Label Smoothing for Temporal Localization of Driving Actions0
Coherent Temporal Synthesis for Incremental Action Segmentation0
Benchmarking Micro-action Recognition: Dataset, Methods, and ApplicationsCode1
Temporal Relations of Informative Frames in Action RecognitionCode0
Video Relationship Detection Using Mixture of ExpertsCode0
Rethinking CLIP-based Video Learners in Cross-Domain Open-Vocabulary Action RecognitionCode1
A Unified Model Selection Technique for Spectral Clustering Based Motion Segmentation0
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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