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 801–850 of 2759 papers

TitleStatusHype
Better Exploiting Motion for Better Action Recognition—0
Ego-Only: Egocentric Action Detection without Exocentric Transferring—0
Action Recognition based Industrial Safety Violation Detection—0
EgoSim: An Egocentric Multi-view Simulator and Real Dataset for Body-worn Cameras during Motion and Activity—0
Fitting, Comparison, and Alignment of Trajectories on Positive Semi-Definite Matrices with Application to Action Recognition—0
Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning—0
Efficient Action Recognition Using Confidence Distillation—0
EgoViT: Pyramid Video Transformer for Egocentric Action Recognition—0
Efficient Action Localization with Approximately Normalized Fisher Vectors—0
Eigen Evolution Pooling for Human Action Recognition—0
EITNet: An IoT-Enhanced Framework for Real-Time Basketball Action Recognition—0
Efficient Action Detection in Untrimmed Videos via Multi-Task Learning—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
Benchmarking Sensitivity of Continual Graph Learning for Skeleton-Based Action Recognition—0
EleAtt-RNN: Adding Attentiveness to Neurons in Recurrent Neural Networks—0
AE-Net:Adjoint Enhancement Network for Efficient Action Recognition in Video Understanding—0
Else-Net: Elastic Semantic Network for Continual Action Recognition From Skeleton Data—0
First Person Action Recognition Using Deep Learned Descriptors—0
Emotion-Based Crowd Representation for Abnormality Detection—0
Emotion Recognition from the perspective of Activity Recognition—0
Effective Action Recognition with Embedded Key Point Shifts—0
EdgeOAR: Real-time Online Action Recognition On Edge Devices—0
End-to-End Joint Semantic Segmentation of Actors and Actions in Video—0
Benchmarking Conventional Vision Models on Neuromorphic Fall Detection and Action Recognition Dataset—0
Action Recognition and State Change Prediction in a Recipe Understanding Task Using a Lightweight Neural Network Model—0
Enhancing Human Action Recognition and Violence Detection Through Deep Learning Audiovisual Fusion—0
Body Joint guided 3D Deep Convolutional Descriptors for Action Recognition—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
Boosting Adversarial Transferability for Skeleton-based Action Recognition via Exploring the Model Posterior Space—0
Enhancing Action Recognition from Low-Quality Skeleton Data via Part-Level Knowledge Distillation—0
A Key Volume Mining Deep Framework for Action Recognition—0
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos—0
Flatten: Video Action Recognition is an Image Classification task—0
Enhancing Video Transformers for Action Understanding with VLM-aided Training—0
Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal Analysis—0
Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition—0
Free-Form Composition Networks for Egocentric Action Recognition—0
BQN: Busy-Quiet Net Enabled by Motion Band-Pass Module for Action Recognition—0
Ensemble One-dimensional Convolution Neural Networks for Skeleton-based Action Recognition—0
FSD-10: A Dataset for Competitive Sports Content Analysis—0
GeoDeformer: Geometric Deformable Transformer for Action Recognition—0
Behavior Recognition Based on the Integration of Multigranular Motion Features—0
Bregman Divergences for Infinite Dimensional Covariance Matrices—0
EA-VTR: Event-Aware Video-Text Retrieval—0
EPIC-KITCHENS-100 Unsupervised Domain Adaptation Challenge for Action Recognition 2022: Team HNU-FPV Technical Report—0
AdvIT: Adversarial Frames Identifier Based on Temporal Consistency in Videos—0
Early Action Recognition with Action Prototypes—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
4InternVideoTop-1 Accuracy77.2—Unverified
5DejaVidTop-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
2OmniVec3-fold Accuracy99.6—Unverified
3VideoMAE V2-g3-fold Accuracy99.6—Unverified
4OmniVec23-fold Accuracy99.6—Unverified
5BIKE3-fold Accuracy98.8—Unverified
6SMART3-fold Accuracy98.64—Unverified
7ZeroI2V ViT-L/143-fold Accuracy98.6—Unverified
8PERF-Net (multi-distilled S3D)3-fold Accuracy98.6—Unverified
9OmniSource (SlowOnly-8x8-R101-RGB + I3D-Flow)3-fold Accuracy98.6—Unverified
10Text4Vis3-fold Accuracy98.2—Unverified