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 1551–1600 of 2759 papers

TitleStatusHype
Dynamic Sampling Networks for Efficient Action Recognition in Videos—0
Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition—0
Dynamic Spatio-Temporal Specialization Learning for Fine-Grained Action Recognition—0
DynamoNet: Dynamic Action and Motion Network—0
EAGLE: Egocentric AGgregated Language-video Engine—0
Early Action Recognition with Action Prototypes—0
EA-VTR: Event-Aware Video-Text Retrieval—0
EdgeOAR: Real-time Online Action Recognition On Edge Devices—0
Effective Action Recognition with Embedded Key Point Shifts—0
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning—0
Efficient Action Localization with Approximately Normalized Fisher Vectors—0
Efficient Action Recognition Using Confidence Distillation—0
Efficient Action Recognition via Dynamic Knowledge Propagation—0
Efficient Attention-free Video Shift Transformers—0
Efficient Feature Extraction, Encoding and Classification for Action Recognition—0
Efficient Modelling Across Time of Human Actions and Interactions—0
Efficient Nonlinear Markov Models for Human Motion—0
Efficient Selective Audio Masked Multimodal Bottleneck Transformer for Audio-Video Classification—0
Efficient Spatialtemporal Context Modeling for Action Recognition—0
Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold—0
EgoAdapt: Adaptive Multisensory Distillation and Policy Learning for Efficient Egocentric Perception—0
Egocentric Action Recognition by Video Attention and Temporal Context—0
Egocentric Activity Recognition and Localization on a 3D Map—0
Egocentric Hand Detection Via Dynamic Region Growing—0
Egocentric Hand Track and Object-based Human Action Recognition—0
Egocentric View Hand Action Recognition by Leveraging Hand Surface and Hand Grasp Type—0
Egocentric zone-aware action recognition across environments—0
Ego-Only: Egocentric Action Detection without Exocentric Transferring—0
EgoPCA: A New Framework for Egocentric Hand-Object Interaction Understanding—0
EgoSim: An Egocentric Multi-view Simulator and Real Dataset for Body-worn Cameras during Motion and Activity—0
Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning—0
EgoViT: Pyramid Video Transformer for Egocentric Action Recognition—0
Eigen Evolution Pooling for Human Action Recognition—0
EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition—0
Elastic Functional Coding of Human Actions: From Vector-Fields to Latent Variables—0
ElderSim: A Synthetic Data Generation Platform for Human Action Recognition in Eldercare Applications—0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks—0
ELSA: Evaluating Localization of Social Activities in Urban Streets using Open-Vocabulary Detection—0
Else-Net: Elastic Semantic Network for Continual Action Recognition From Skeleton Data—0
Embedding Symbolic Temporal Knowledge into Deep Sequential Models—0
Emotion-Based Crowd Representation for Abnormality Detection—0
Emotion Recognition from the perspective of Activity Recognition—0
End-to-End Joint Semantic Segmentation of Actors and Actions in Video—0
End-to-End Spatio-Temporal Action Localisation with Video Transformers—0
Energy-based Periodicity Mining with Deep Features for Action Repetition Counting in Unconstrained Videos—0
Enhanced skeleton visualization for view invariant human action recognition—0
Enhanced Spatiotemporal Prediction Using Physical-guided And Frequency-enhanced Recurrent Neural Networks—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
Enhancing Video Transformers for Action Understanding with VLM-aided Training—0
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis—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