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3D Action Recognition

Image: Rahmani et al

Papers

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Showing 1–50 of 91 papers

TitleStatusHype
A System for Real-Time Interactive Analysis of Deep Learning TrainingCode2
TSM: Temporal Shift Module for Efficient Video UnderstandingCode1
STARS: Self-supervised Tuning for 3D Action Recognition in Skeleton SequencesCode1
CHASE: Learning Convex Hull Adaptive Shift for Skeleton-based Multi-Entity Action RecognitionCode1
CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual DistillationCode1
3DV: 3D Dynamic Voxel for Action Recognition in Depth VideoCode1
Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action RecognitionCode1
Unsupervised Learning of Object Keypoints for Perception and ControlCode1
Real-time 3D human action recognition based on Hyperpoint sequenceCode1
Weight Excitation: Built-in Attention Mechanisms in Convolutional Neural NetworksCode1
Masked Motion Predictors are Strong 3D Action Representation LearnersCode1
Grad-CAM++: Improved Visual Explanations for Deep Convolutional NetworksCode1
Deep Hierarchical Representation of Point Cloud Videos via Spatio-Temporal DecompositionCode1
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action RecognitionCode1
3DVNet: Multi-View Depth Prediction and Volumetric RefinementCode1
Revisiting Skeleton-based Action RecognitionCode1
No Pain, Big Gain: Classify Dynamic Point Cloud Sequences with Static Models by Fitting Feature-level Space-time SurfacesCode1
Reconstructing undersampled photoacoustic microscopy images using deep learningCode1
On the Utility of 3D Hand Poses for Action RecognitionCode1
BABEL: Bodies, Action and Behavior with English LabelsCode1
Point 4D Transformer Networks for Spatio-Temporal Modeling in Point Cloud VideosCode1
Disentangling and Unifying Graph Convolutions for Skeleton-Based Action RecognitionCode1
PSTNet: Point Spatio-Temporal Convolution on Point Cloud SequencesCode1
Interactive Spatiotemporal Token Attention Network for Skeleton-based General Interactive Action RecognitionCode1
Domain Knowledge-Informed Self-Supervised Representations for Workout Form AssessmentCode1
Kernelized Covariance for Action Recognition—0
Learning clip representations for skeleton-based 3d action recognition—0
Learning Scene Flow With Skeleton Guidance For 3D Action Recognition—0
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks—0
Localized Trajectories for 2D and 3D Action Recognition—0
Mimetics: Towards Understanding Human Actions Out of Context—0
Mining Mid-level Features for Action Recognition Based on Effective Skeleton Representation—0
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks—0
Neural Graph Matching Networks for Fewshot 3D Action Recognition—0
Order-Preserving Wasserstein Discriminant Analysis—0
PRENet: A Plane-Fit Redundancy Encoding Point Cloud Sequence Network for Real-Time 3D Action Recognition—0
Real-time Human Action Recognition Using Locally Aggregated Kinematic-Guided Skeletonlet and Supervised Hashing-by-Analysis Model—0
Histogram of Oriented Principal Components for Cross-View Action Recognition—0
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning—0
A Compact Kernel Approximation for 3D Action Recognition—0
A Deep Learning Approach for Real-Time 3D Human Action Recognition from Skeletal Data—0
Adversarial Self-Supervised Learning for Semi-Supervised 3D Action Recognition—0
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition—0
A New Representation of Skeleton Sequences for 3D Action Recognition—0
A Tube-and-Droplet-based Approach for Representing and Analyzing Motion Trajectories—0
Coding Kendall's Shape Trajectories for 3D Action Recognition—0
Combining Deep Learning Classifiers for 3D Action Recognition—0
CrossGLG: LLM Guides One-shot Skeleton-based 3D Action Recognition in a Cross-level Manner—0
Deep Learning on Lie Groups for Skeleton-based Action Recognition—0
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks—0
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All datasetsAssembly101NTU RGB+D100 sleep nights of 8 caregivers

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1HandFormer-B/21Actions Top-141.06—Unverified
2TSMActions Top-135.27—Unverified
3RGBPoseConv3DActions Top-133.61—Unverified
4MS-G3DActions Top-128.7—Unverified
5ISTA-NetActions Top-128.07—Unverified
6CHASE(CTR-GCN)Actions Top-128.03—Unverified
72s-AGCNActions Top-126.7—Unverified
#ModelMetricClaimedVerifiedStatus
1KinetCross Subject Accuracy92.3—Unverified
2PSTNet++Cross Subject Accuracy91.4—Unverified
3PSTNetCross Subject Accuracy90.5—Unverified
4P4TransformerCross Subject Accuracy90.2—Unverified
53DV-PointNet++Cross Subject Accuracy88.8—Unverified
#ModelMetricClaimedVerifiedStatus
1htf10%4—Unverified