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Action-Attending Graphic Neural Network
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11744047_45openalex-snapshot
Region Covariance: A Fast Descriptor for Detection and Classification
inferredcites →https://doi.org/10.1007/978-3-319-02714-2_6openalex-snapshot
Dynamic Feature Selection for Online Action Recognition
inferredcites →https://doi.org/10.1007/978-3-319-10605-2_2openalex-snapshot
From Manifold to Manifold: Geometry-Aware Dimensionality Reduction for SPD Matrices
inferredcites →https://doi.org/10.1007/978-3-319-46478-7_23openalex-snapshot
Graph Based Skeleton Motion Representation and Similarity Measurement for Action Recognition
inferredcites →https://doi.org/10.1007/978-3-319-46487-9_50openalex-snapshot
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
inferredcites →https://doi.org/10.1007/978-3-319-91863-1_8openalex-snapshot
A Large Scale RGB-D Dataset for Action Recognition
inferredcites →https://doi.org/10.1007/978-3-642-25446-8_4openalex-snapshot
Sequential Deep Learning for Human Action Recognition
inferredcites →https://doi.org/10.1007/978-3-642-44964-2_8openalex-snapshot
A Survey on Human Motion Analysis from Depth Data
inferredcites →https://doi.org/10.1007/s11263-012-0550-7openalex-snapshot
Exploring the Trade-off Between Accuracy and Observational Latency in Action Recognition
inferredcites →https://doi.org/10.1016/j.acha.2010.04.005openalex-snapshot
Wavelets on graphs via spectral graph theory
inferredcites →https://doi.org/10.1016/j.jvcir.2013.04.007openalex-snapshot
Sequence of the most informative joints (SMIJ): A new representation for human skeletal action recognition
inferredcites →https://doi.org/10.1016/j.patcog.2014.08.011openalex-snapshot
Accurate 3D action recognition using learning on the Grassmann manifold
inferredcites →https://doi.org/10.1038/scientificamerican0675-76openalex-snapshot
Visual Motion Perception
inferredcites →https://doi.org/10.1109/cvpr.2010.5539962openalex-snapshot
Modeling pixel means and covariances using factorized third-order boltzmann machines
inferredcites →https://doi.org/10.1109/cvpr.2011.5995316openalex-snapshot
Real-time human pose recognition in parts from single depth images
inferredcites →https://doi.org/10.1109/cvpr.2012.6247813openalex-snapshot
Mining actionlet ensemble for action recognition with depth cameras
inferredcites →https://doi.org/10.1109/cvpr.2013.98openalex-snapshot
HON4D: Histogram of Oriented 4D Normals for Activity Recognition from Depth Sequences
inferredcites →https://doi.org/10.1109/cvpr.2014.82openalex-snapshot
Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group
inferredcites →https://doi.org/10.1109/cvpr.2015.7298714openalex-snapshot
Hierarchical recurrent neural network for skeleton based action recognition
inferredcites →https://doi.org/10.1109/cvpr.2015.7299172openalex-snapshot
Jointly learning heterogeneous features for RGB-D activity recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.115openalex-snapshot
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
inferredcites →https://doi.org/10.1109/cvpr.2017.137openalex-snapshot
Deep Learning on Lie Groups for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/cvpr.2017.391openalex-snapshot
Global Context-Aware Attention LSTM Networks for 3D Action Recognition
inferredcites →https://doi.org/10.1109/cvprw.2010.5543273openalex-snapshot
Action recognition based on a bag of 3D points
inferredcites →https://doi.org/10.1109/cvprw.2012.6239175openalex-snapshot
G3D: A gaming action dataset and real time action recognition evaluation framework
inferredcites →https://doi.org/10.1109/cvprw.2012.6239233openalex-snapshot
View invariant human action recognition using histograms of 3D joints
inferredcites →https://doi.org/10.1109/cvprw.2013.153openalex-snapshot
Bio-inspired Dynamic 3D Discriminative Skeletal Features for Human Action Recognition
inferredcites →https://doi.org/10.1109/cvprw.2013.77openalex-snapshot
Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses
inferredcites →https://doi.org/10.1109/iccv.2011.6126548openalex-snapshot
A “string of feature graphs” model for recognition of complex activities in natural videos
inferredcites →https://doi.org/10.1109/iccv.2013.227openalex-snapshot
Group Sparsity and Geometry Constrained Dictionary Learning for Action Recognition from Depth Maps
inferredcites →https://doi.org/10.1109/iccv.2013.342openalex-snapshot
The Moving Pose: An Efficient 3D Kinematics Descriptor for Low-Latency Action Recognition and Detection
inferredcites →https://doi.org/10.1109/iccv.2013.393openalex-snapshot
Directed Acyclic Graph Kernels for Action Recognition
inferredcites →https://doi.org/10.1109/iccv.2015.519openalex-snapshot
Beyond Covariance: Feature Representation with Nonlinear Kernel Matrices
inferredcites →https://doi.org/10.1109/iccvw.2011.6130379openalex-snapshot
RGBD-HuDaAct: A color-depth video database for human daily activity recognition
inferredcites →https://doi.org/10.1109/iccvw.2015.48openalex-snapshot
Moving Poselets: A Discriminative and Interpretable Skeletal Motion Representation for Action Recognition
inferredcites →https://doi.org/10.1109/icip.2015.7350781openalex-snapshot
UTD-MHAD: A multimodal dataset for human action recognition utilizing a depth camera and a wearable inertial sensor
inferredcites →https://doi.org/10.1109/icip.2015.7351578openalex-snapshot
Action recognition using joint coordinates of 3D skeleton data
inferredcites →https://doi.org/10.1109/icpr.2014.772openalex-snapshot
Skeletal Quads: Human Action Recognition Using Joint Quadruples
inferredcites →https://doi.org/10.1109/icra.2012.6224591openalex-snapshot
Unstructured human activity detection from RGBD images
inferredcites →https://doi.org/10.1109/msp.2012.2235192openalex-snapshot
The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains
inferredcites →https://doi.org/10.1109/tcyb.2014.2350774openalex-snapshot
3-D Human Action Recognition by Shape Analysis of Motion Trajectories on Riemannian Manifold
inferredcites →https://doi.org/10.1109/thms.2015.2504550openalex-snapshot
Action Recognition From Depth Maps Using Deep Convolutional Neural Networks
inferredcites →https://doi.org/10.1109/tip.2016.2552404openalex-snapshot
Representation Learning of Temporal Dynamics for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/tmm.2015.2505089openalex-snapshot
Effective Active Skeleton Representation for Low Latency Human Action Recognition
inferredcites →https://doi.org/10.1109/tmm.2016.2626959openalex-snapshot
Discriminative Multi-instance Multitask Learning for 3D Action Recognition
inferredcites →https://doi.org/10.1109/tpami.2013.198openalex-snapshot
Learning Actionlet Ensemble for 3D Human Action Recognition
inferredcites →https://doi.org/10.1109/tpami.2015.2439257openalex-snapshot
Action Recognition Using Rate-Invariant Analysis of Skeletal Shape Trajectories
inferredcites →https://doi.org/10.1109/tpami.2016.2640292openalex-snapshot
Jointly Learning Heterogeneous Features for RGB-D Activity Recognition
inferredcites →https://doi.org/10.1145/2733373.2806296openalex-snapshot
ConvNets-Based Action Recognition from Depth Maps through Virtual Cameras and Pseudocoloring
inferredcites →https://doi.org/10.1145/3065386openalex-snapshot
ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1177/0278364913478446openalex-snapshot
Learning human activities and object affordances from RGB-D videos
inferredcites →https://doi.org/10.1609/aaai.v31i1.10866openalex-snapshot
A Riemannian Network for SPD Matrix Learning
inferredcites →https://doi.org/10.1609/aaai.v31i1.11212openalex-snapshot
An End-to-End Spatio-Temporal Attention Model for Human Action Recognition from Skeleton Data
inferredcites →https://doi.org/10.4230/lipics.fun.2016.3openalex-snapshot
Generating Sequences With Recurrent Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1312.6203openalex-snapshot
Spectral Networks and Locally Connected Networks on Graphs
inferredcites →https://doi.org/10.48550/arxiv.1409.0473openalex-snapshot
Neural Machine Translation by Jointly Learning to Align and Translate
inferredcites →https://doi.org/10.48550/arxiv.1409.3215openalex-snapshot
Sequence to Sequence Learning with Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1502.03044openalex-snapshot
Show, Attend and Tell: Neural Image Caption Generation with Visual Attention
inferredcites →https://doi.org/10.48550/arxiv.1502.04681openalex-snapshot
Unsupervised Learning of Video Representations using LSTMs
inferredcites →https://doi.org/10.48550/arxiv.1506.07503openalex-snapshot
Attention-Based Models for Speech Recognition
inferredcites →https://doi.org/10.48550/arxiv.1605.05273openalex-snapshot
Learning Convolutional Neural Networks for Graphs
inferredcites →https://doi.org/10.48550/arxiv.1612.05877openalex-snapshot
Deep Learning on Lie Groups for Skeleton-based Action Recognition
inferredcites →https://doi.org/10.5281/zenodo.1286817openalex-snapshot
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Skeleton-Based Activity Recognition: Preprocessing and Approaches
inferred← citeshttps://doi.org/10.1007/s10462-025-11442-0openalex-snapshot
Graph network learning for human skeleton modeling: a survey
inferred← citeshttps://doi.org/10.1007/s10668-022-02585-zopenalex-snapshot
Big data analytics-based traffic flow forecasting using inductive spatial-temporal network
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Multi-view deep generative dual fusion network for zero-shot learning
inferred← citeshttps://doi.org/10.1007/s11390-023-3140-yopenalex-snapshot
PESTA: An Elastic Motion Capture Data Retrieval Method
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A new design scheme of two-channel graph filter bank
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Spatiotemporal interactive multiple self-attention network for skeleton-based action recognition
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Scene understanding method utilizing global visual and spatial interaction features for safety production
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Sparseness embedding in bending of space and time; a case study on unsupervised 3D action recognition
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Exploiting spatio-temporal representation for 3D human action recognition from depth map sequences
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A novel structure automatic-determined Fourier extreme learning machine for generalized Black–Scholes partial differential equation
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Exploring a rich spatial–temporal dependent relational model for skeleton-based action recognition by bidirectional LSTM-CNN
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Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations
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Study on the allosteric activation mechanism of SHP2 via elastic network models and neural relational inference molecular dynamics simulation
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A 3D graph convolutional networks model for 2D skeleton‐based human action recognition
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Human Action Performance Using Deep Neuro-Fuzzy Recurrent Attention Model
inferred← citeshttps://doi.org/10.1109/access.2020.2992740openalex-snapshot
Global Spatio-Temporal Attention for Action Recognition Based on 3D Human Skeleton Data
inferred← citeshttps://doi.org/10.1109/access.2024.3520172openalex-snapshot
MSA-GCN: Exploiting Multi-Scale Temporal Dynamics With Adaptive Graph Convolution for Skeleton-Based Action Recognition
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Sports Action Pattern Recognition Method Based on Fuzzy Neural Network Theory
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Action Recognition with Domain Invariant Features of Skeleton Image
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Using Language-Aligned Gesture Embeddings for Understanding Gestures Accompanying Math Terms
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Overlapped State Hidden Semi-Markov Model for Grouped Multiple Sequences
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Cross-Graph Convolution Learning for Large-Scale Text-Picture Shopping Guide in E-Commerce Search
inferred← citeshttps://doi.org/10.1109/iceect61758.2024.10739243openalex-snapshot
Human Action Recognition Using Pose-Guided Graph Convolutional Networks and Long Short-Term Memory
inferred← citeshttps://doi.org/10.1109/icip.2019.8803186openalex-snapshot
Graph Based Skeleton Modeling for Human Activity Analysis
inferred← citeshttps://doi.org/10.1109/icpr.2018.8546012openalex-snapshot
Action Recognition with Visual Attention on Skeleton Images
inferred← citeshttps://doi.org/10.1109/ijcnn.2019.8851767openalex-snapshot
Si-GCN: Structure-induced Graph Convolution Network for Skeleton-based Action Recognition
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A Secure Approach for Human Computer Interaction Using Human Hand Action
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Graph Convolutional Neural Network for Human Action Recognition: A Comprehensive Survey
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Action Recognition With Spatio–Temporal Visual Attention on Skeleton Image Sequences
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3D Skeleton-Based Human Motion Prediction Using Dynamic Multi-Scale Spatiotemporal Graph Recurrent Neural Networks
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Video Captioning With Object-Aware Spatio-Temporal Correlation and Aggregation
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Hypergraph Neural Network for Skeleton-Based Action Recognition
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Human Interaction Understanding With Joint Graph Decomposition and Node Labeling
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Multi-Modal Interaction Graph Convolutional Network for Temporal Language Localization in Videos
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Attention-Based Multiview Re-Observation Fusion Network for Skeletal Action Recognition
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GAIM: Graph Attention Interaction Model for Collective Activity Recognition
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Multi-Localized Sensitive Autoencoder-Attention-LSTM For Skeleton-based Action Recognition
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Context-Dependent Diffusion Network for Visual Relationship Detection
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Hashing Graph Convolution for Node Classification
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Dual-Stream Structured Graph Convolution Network for Skeleton-Based Action Recognition
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Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition
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A Deep Attention Model for Action Recognition from Skeleton Data
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Variable Rate Independently Recurrent Neural Network (IndRNN) for Action Recognition
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Human Action Recognition of Spatiotemporal Parameters for Skeleton Sequences Using MTLN Feature Learning Framework
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Silhouette Pose Feature-Based Human Action Classification Using Capsule Network
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