On the public citation graph

Harnessing Time: A Skeleton Temporal Fusion Graph Convolutional Network for Action Recognition

https://doi.org/10.2139/ssrn.4646561
0 signed · 30 inferred · view as graph

Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.

Is this your work? Sign in with ORCID and put your name on the claims that are yours.
Claim with ORCID
Reference health. See whether every reference in this work resolves to a known work and none has been retracted — and get a dated CiteStamped badge you can embed.
See reference health

What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-01246-5_7openalex-extracted
Skeleton-Based Action Recognition with Spatial Reasoning and Temporal Stack Learning
inferredcites →https://doi.org/10.1007/978-3-030-58586-0_32openalex-extracted
Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1007/978-3-319-46487-9_50openalex-extracted
Spatio-Temporal LSTM with Trust Gates for 3D Human Action Recognition
inferredcites →https://doi.org/10.1007/s11263-022-01594-9openalex-extracted
Human Action Recognition and Prediction: A Survey
inferredcites →https://doi.org/10.1049/cit2.12012openalex-extracted
A spatial attentive and temporal dilated (SATD) GCN for skeleton‐based action recognition
inferredcites →https://doi.org/10.1109/cvpr.2016.115openalex-extracted
NTU RGB+D: A Large Scale Dataset for 3D Human Activity Analysis
inferredcites →https://doi.org/10.1109/cvpr.2017.387openalex-extracted
Modeling Temporal Dynamics and Spatial Configurations of Actions Using Two-Stream Recurrent Neural Networks
inferredcites →https://doi.org/10.1109/cvpr.2019.00132openalex-extracted
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.00371openalex-extracted
Actional-Structural Graph Convolutional Networks for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/cvpr.2019.01230openalex-extracted
Two-Stream Adaptive Graph Convolutional Networks for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/cvpr46437.2021.01444openalex-extracted
Bottom-Up Human Pose Estimation Via Disentangled Keypoint Regression
inferredcites →https://doi.org/10.1109/cvprw.2019.00123openalex-extracted
Three-Stream Convolutional Neural Network With Multi-Task and Ensemble Learning for 3D Action Recognition
inferredcites →https://doi.org/10.1109/mmul.2012.24openalex-extracted
Microsoft Kinect Sensor and Its Effect
inferredcites →https://doi.org/10.1109/tcsvt.2020.3015051openalex-extracted
Richly Activated Graph Convolutional Network for Robust Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/tnnls.2021.3061115openalex-extracted
Memory Attention Networks for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/tpami.2019.2898954openalex-extracted
Skeleton-Based Online Action Prediction Using Scale Selection Network
inferredcites →https://doi.org/10.1109/tpami.2019.2916873openalex-extracted
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
inferredcites →https://doi.org/10.1109/tpami.2022.3157033openalex-extracted
Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/tpami.2022.3222784openalex-extracted
AlphaPose: Whole-Body Regional Multi-Person Pose Estimation and Tracking in Real-Time
inferredcites →https://doi.org/10.1609/aaai.v32i1.12328openalex-extracted
Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1609/aaai.v34i03.5652openalex-extracted
Learning Graph Convolutional Network for Skeleton-Based Human Action Recognition by Neural Searching
inferredcites →https://doi.org/10.1609/aaai.v34i07.6759openalex-extracted
Part-Level Graph Convolutional Network for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1609/aaai.v35i2.16197openalex-extracted
Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.48550/arxiv.1801.07455openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1804.06055openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1902.09130openalex-extracted
An Attention Enhanced Graph Convolutional LSTM Network for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.48550/arxiv.1911.04131openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2206.13028openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.2211.03375openalex-extracted
inferredcites →https://doi.org/10.24963/ijcai.2018/109openalex-snapshot
Co-occurrence Feature Learning from Skeleton Data for Action Recognition and Detection with Hierarchical Aggregation
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.