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CAGTN: A Lightweight Confidence-Aware Graph-Temporal Network for Sign Language Recognition in Resource-Constrained Settings

https://doi.org/10.2139/ssrn.6864645
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1109/access.2023.3247761openalex-snapshot
Signgraph: An Efficient and Accurate Pose-Based Graph Convolution Approach Toward Sign Language Recognition
inferredcites →https://doi.org/10.1109/tcsvt.2018.2870740openalex-snapshot
Attention-Based 3D-CNNs for Large-Vocabulary Sign Language Recognition
inferredcites →https://doi.org/10.1109/tetci.2024.3509500openalex-snapshot
MSE-GCN: A Multiscale Spatiotemporal Feature Aggregation Enhanced Efficient Graph Convolutional Network for Dynamic Sign Language Recognition
inferredcites →https://doi.org/10.1109/tip.2020.3028207openalex-snapshot
Skeleton-Based Action Recognition With Multi-Stream Adaptive Graph Convolutional Networks
inferredcites →https://doi.org/10.1109/tpami.2019.2929257openalex-snapshot
OpenPose: Realtime Multi-Person 2D Pose Estimation Using Part Affinity Fields
inferredcites →https://doi.org/10.1109/tpami.2022.3157033openalex-snapshot
Constructing Stronger and Faster Baselines for Skeleton-Based Action Recognition
inferredcites →https://doi.org/10.1109/tpami.2023.3269220openalex-snapshot
SignBERT+: Hand-Model-Aware Self-Supervised Pre-Training for Sign Language Understanding
inferredcites →https://doi.org/10.3390/electronics13071229openalex-snapshot
Isolated Video-Based Sign Language Recognition Using a Hybrid CNN-LSTM Framework Based on Attention Mechanism
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