SOTAVerified

3D Action Recognition

Image: Rahmani et al

Papers

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Showing 51–75 of 91 papers

TitleStatusHype
DWnet: Deep-Wide Network for 3D Action Recognition—0
Dynamic Graph Modules for Modeling Object-Object Interactions in Activity Recognition—0
Efficient Temporal Sequence Comparison and Classification Using Gram Matrix Embeddings on a Riemannian Manifold—0
First-Take-All: Temporal Order-Preserving Hashing for 3D Action Videos—0
Fusing multiple features for depth-based action recognition—0
Global Context-Aware Attention LSTM Networks for 3D Action Recognition—0
Graph Partitioning and Graph Neural Network based Hierarchical Graph Matching for Graph Similarity Computation—0
CoSimGNN: Towards Large-scale Graph Similarity Computation—0
3D Action Recognition From Novel Viewpoints—0
HOPC: Histogram of Oriented Principal Components of 3D Pointclouds for Action Recognition—0
Independent Sign Language Recognition with 3D Body, Hands, and Face Reconstruction—0
KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition—0
Self-Supervised 3D Action Representation Learning with Skeleton Cloud Colorization—0
Skeletal Movement to Color Map: A Novel Representation for 3D Action Recognition with Inception Residual Networks—0
Skeleton based Activity Recognition by Fusing Part-wise Spatio-temporal and Attention Driven Residues—0
Skeleton Cloud Colorization for Unsupervised 3D Action Representation Learning—0
Spatial-Temporal Transformer for 3D Point Cloud Sequences—0
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition—0
Tensor Representations via Kernel Linearization for Action Recognition from 3D Skeletons (Extended Version)—0
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition—0
Towards Efficient Coarse-to-Fine Networks for Action and Gesture Recognition—0
Train, Diagnose and Fix: Interpretable Approach for Fine-grained Action Recognition—0
Two-Stream 3D Convolutional Neural Network for Skeleton-Based Action Recognition—0
Unsupervised View-Invariant Human Posture Representation—0
VI-Net: View-Invariant Quality of Human Movement Assessment—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