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 1051–1100 of 2759 papers

TitleStatusHype
Graph-based Spatial-temporal Feature Learning for Neuromorphic Vision SensingCode0
Generative Hierarchical Temporal Transformer for Hand Pose and Action Modeling—0
Composable Augmentation Encoding for Video Representation Learning—0
Generating Human Action Videos by Coupling 3D Game Engines and Probabilistic Graphical Models—0
Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition—0
Complex Video Action Reasoning via Learnable Markov Logic Network—0
Generalized Zero-Shot Learning for Action Recognition with Web-Scale Video Data—0
Generalized Rank Pooling for Activity Recognition—0
Optimized Skeleton-based Action Recognition via Sparsified Graph Regression—0
Hierarchical Graph Convolutional Skeleton Transformer for Action Recognition—0
Complex Human Action Recognition in Live Videos Using Hybrid FR-DL Method—0
Ani-GIFs: A benchmark dataset for domain generalization of action recognition from GIFs—0
Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion—0
GCF-Net: Gated Clip Fusion Network for Video Action Recognition—0
Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark—0
Comparative Evaluation of Action Recognition Methods via Riemannian Manifolds, Fisher Vectors and GMMs: Ideal and Challenging Conditions—0
GAN for Vision, KG for Relation: a Two-stage Deep Network for Zero-shot Action Recognition—0
Future Aspects in Human Action Recognition: Exploring Emerging Techniques and Ethical Influences—0
A New Representation of Skeleton Sequences for 3D Action Recognition—0
Action Recognition via Pose-Based Graph Convolutional Networks with Intermediate Dense Supervision—0
ActAR: Actor-Driven Pose Embeddings for Video Action Recognition—0
FuTH-Net: Fusing Temporal Relations and Holistic Features for Aerial Video Classification—0
CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile Devices—0
C3T: Cross-modal Transfer Through Time for Sensor-based Human Activity Recognition—0
Fusing multiple features for depth-based action recognition—0
Combining Spatio-Temporal Appearance Descriptors and Optical Flow for Human Action Recognition in Video Data—0
A New Adjacency Matrix Configuration in GCN-based Models for Skeleton-based Action Recognition—0
Fusing Hand and Body Skeletons for Human Action Recognition in Assembly—0
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification—0
Combining Deep Learning Classifiers for 3D Action Recognition—0
Fundamental Limits of Transfer Learning in Binary Classifications—0
Fully-Coupled Two-Stream Spatiotemporal Networks for Extremely Low Resolution Action Recognition—0
Combining ConvNets with Hand-Crafted Features for Action Recognition Based on an HMM-SVM Classifier—0
A New Action Recognition Framework for Video Highlights Summarization in Sporting Events—0
FSD-10: A Dataset for Competitive Sports Content Analysis—0
Combined CNN Transformer Encoder for Enhanced Fine-grained Human Action Recognition—0
FSAR: Federated Skeleton-based Action Recognition with Adaptive Topology Structure and Knowledge Distillation—0
Combating Missing Modalities in Egocentric Videos at Test Time—0
Colo-SCRL: Self-Supervised Contrastive Representation Learning for Colonoscopic Video Retrieval—0
From Synsets to Videos: Enriching ItalWordNet Multimodally—0
Collecting and Annotating the Large Continuous Action Dataset—0
An End-to-End Two-Stream Network Based on RGB Flow and Representation Flow for Human Action Recognition—0
Action Recognition Utilizing YGAR Dataset—0
A Cross-Dataset Study for Text-based 3D Human Motion Retrieval—0
From Pose to Activity: Surveying Datasets and Introducing CONVERSE—0
From Image to Video: An Empirical Study of Diffusion Representations—0
From Detection to Action Recognition: An Edge-Based Pipeline for Robot Human Perception—0
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data—0
From CNNs to Transformers in Multimodal Human Action Recognition: A Survey—0
From Actions to Events: A Transfer Learning Approach Using Improved Deep Belief Networks—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