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

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Showing 2351–2400 of 2759 papers

TitleStatusHype
Free-Form Composition Networks for Egocentric Action Recognition—0
Frequency-aware Event Cloud Network—0
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks—0
From CNNs to Transformers in Multimodal Human Action Recognition: A Survey—0
From Detection to Action Recognition: An Edge-Based Pipeline for Robot Human Perception—0
From Image to Video: An Empirical Study of Diffusion Representations—0
From Pose to Activity: Surveying Datasets and Introducing CONVERSE—0
From Synsets to Videos: Enriching ItalWordNet Multimodally—0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation—0
FSD-10: A Dataset for Competitive Sports Content Analysis—0
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition—0
Fundamental Limits of Transfer Learning in Binary Classifications—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
Fusing Hand and Body Skeletons for Human Action Recognition in Assembly—0
Fusing multiple features for depth-based action recognition—0
C3T: Cross-modal Transfer Through Time for Sensor-based Human Activity Recognition—0
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification—0
Future Aspects in Human Action Recognition: Exploring Emerging Techniques and Ethical Influences—0
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition—0
GCF-Net: Gated Clip Fusion Network for Video Action Recognition—0
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition—0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression—0
Generalized Rank Pooling for Activity Recognition—0
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data—0
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition—0
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models—0
Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling—0
Generative Multi-View Human Action Recognition—0
GeoDeformer: Geometric Deformable Transformer for Action Recognition—0
Geometric Deep Neural Network Using Rigid and Non-Rigid Transformations for Human Action Recognition—0
Geometry Guided Convolutional Neural Networks for Self-Supervised Video Representation Learning—0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition—0
Global Temporal Difference Network for Action Recognition—0
Global Temporal Representation based CNNs for Infrared Action Recognition—0
GoferBot: A Visual Guided Human-Robot Collaborative Assembly System—0
Going Deeper into Action Recognition: A Survey—0
Going Deeper into First-Person Activity Recognition—0
Going Deeper into Recognizing Actions in Dark Environments: A Comprehensive Benchmark Study—0
GPT4Ego: Unleashing the Potential of Pre-trained Models for Zero-Shot Egocentric Action Recognition—0
Gradient Boundary Histograms for Action Recognition—0
Gradient Forward-Propagation for Large-Scale Temporal Video Modelling—0
Gradient Frequency Modulation for Visually Explaining Video Understanding Models—0
Gradient Weighted Superpixels for Interpretability in CNNs—0
GradMix: Multi-source Transfer across Domains and Tasks—0
Graph-Based High-Order Relation Modeling for Long-Term Action Recognition—0
Graph Based Skeleton Modeling for Human Activity Analysis—0
Graph Convolutional Module for Temporal Action Localization in Videos—0
Graph Edge Convolutional Neural Networks for Skeleton Based Action Recognition—0
Graph learning in robotics: a survey—0
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation—0
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All datasetsSomething-Something V2UCF101HMDB-51Something-Something V1AVA v2.2EPIC-KITCHENS-100NTU RGB+DNTU RGB+D 120Diving-48ActivityNetAVA v2.1H2O (2 Hands and Objects)

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MViTv2-B (IN-21K + Kinetics400 pretrain)Top-5 Accuracy93.4—Unverified
2RSANet-R50 (8+16 frames, ImageNet pretrained, 2 clips)Top-5 Accuracy91.1—Unverified
3MVD (Kinetics400 pretrain, ViT-H, 16 frame)Top-1 Accuracy77.3—Unverified
4DejaVidTop-1 Accuracy77.2—Unverified
5InternVideoTop-1 Accuracy77.2—Unverified
6InternVideo2-1BTop-1 Accuracy77.1—Unverified
7VideoMAE V2-gTop-1 Accuracy77—Unverified
8MVD (Kinetics400 pretrain, ViT-L, 16 frame)Top-1 Accuracy76.7—Unverified
9Hiera-L (no extra data)Top-1 Accuracy76.5—Unverified
10TubeViT-LTop-1 Accuracy76.1—Unverified
#ModelMetricClaimedVerifiedStatus
1FTP-UniFormerV2-L/143-fold Accuracy99.7—Unverified
2OmniVec23-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec3-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
10LGD-3D Two-stream3-fold Accuracy98.2—Unverified