Generative Action Description Prompts for Skeleton-based Action Recognition
Author:
Affiliation:
1. The Hong Kong Polytechnic University
2. DAMO Academy, Alibaba Group
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10376473/10376477/10377553.pdf?arnumber=10377553
Reference46 articles.
1. Language models are few-shot learners;Brown,2020
2. Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition
3. Multi-Scale Spatial Temporal Graph Convolutional Network for Skeleton-Based Action Recognition
4. Decoupling GCN with DropGraph Module for Skeleton-Based Action Recognition
5. Skeleton-Based Action Recognition With Shift Graph Convolutional Network
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2. Linguistic-Driven Partial Semantic Relevance Learning for Skeleton-Based Action Recognition;Sensors;2024-07-26
3. Multimodal vision-based human action recognition using deep learning: a review;Artificial Intelligence Review;2024-06-19
4. Recognition of Endotracheal Suctioning Activities: A Feature Extraction and Ensemble Learning Approach Based on Pose Estimation Data;2024 International Conference on Activity and Behavior Computing (ABC);2024-05-29
5. A Survey on 3D Skeleton-Based Action Recognition Using Learning Method;Cyborg and Bionic Systems;2024-01
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