Self-Supervised Hypergraph Learning for Enhanced Multimodal Representation

Self-Supervised Hypergraph Learning for Enhanced Multimodal Representation

Hypergraph neural networks have gained substantial popularity in capturing complex correlations between data items in multimodal datasets. In this study, we propose a novel approach called the self-supervised hypergraph learning (SHL) framework that focuses on extracting hypergraph features to impro...

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Bibliographic Details
Main Authors: Hongji Shu, Chaojun Meng, Pasquale De Meo, Qing Wang, Jia Zhu
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Multimodal
micro-video
self-supervised learning
hypergraph neural networks
Online Access:https://ieeexplore.ieee.org/document/10418926/

Internet

https://ieeexplore.ieee.org/document/10418926/

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