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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Autors principals:	Hongji Shu, Chaojun Meng, Pasquale De Meo, Qing Wang, Jia Zhu
Format:	Artigo
Idioma:	Inglês
Publicat:	IEEE 2024-01-01
Col·lecció:	IEEE Access
Matèries:	Multimodal micro-video self-supervised learning hypergraph neural networks
Accés en línia:	https://ieeexplore.ieee.org/document/10418926/
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