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3D Action Recognition

Image: Rahmani et al

Papers

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Showing 51–75 of 91 papers

TitleStatusHype
Temporal Transformer Networks: Joint Learning of Invariant and Discriminative Time WarpingCode0
Localized Trajectories for 2D and 3D Action Recognition—0
Learning to Recognize 3D Human Action from A New Skeleton-based Representation Using Deep Convolutional Neural Networks—0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition—0
TSM: Temporal Shift Module for Efficient Video UnderstandingCode1
Neural Graph Matching Networks for Fewshot 3D Action Recognition—0
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks—0
3D Human Action Recognition with Siamese-LSTM Based Deep Metric Learning—0
Leolani: a reference machine with a theory of mind for social communicationCode0
Coding Kendall's Shape Trajectories for 3D Action Recognition—0
A Fine-to-Coarse Convolutional Neural Network for 3D Human Action Recognition—0
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action RecognitionCode0
Depth Pooling Based Large-scale 3D Action Recognition with Convolutional Neural Networks—0
Learning clip representations for skeleton-based 3d action recognition—0
Fisherposes for Human Action Recognition Using Kinect Sensor DataCode0
Robust 3D Action Recognition through Sampling Local Appearances and Global Distributions—0
Scalable and Compact 3D Action Recognition with Approximated RBF Kernel Machines—0
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition—0
Grad-CAM++: Improved Visual Explanations for Deep Convolutional NetworksCode1
A Compact Kernel Approximation for 3D Action Recognition—0
Recognizing Involuntary Actions from 3D Skeleton Data Using Body StatesCode0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition—0
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition—0
Investigation of Different Skeleton Features for CNN-based 3D Action RecognitionCode0
Interpretable 3D Human Action Analysis with Temporal Convolutional NetworksCode0
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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