.png){kind=icon}
Return
Scalable multi-modal representation learning networks
.png){kind=icon}
OA
AI
DOI：10.1007/s10462-025-11224-8.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Multi-modal representation learning is recognized for its comprehensive interpretation across diverse modalities. Although existing approaches have yielded favorable results, they face challenges in high-order information preservation and out-of-sample data generalization. To tackle these issues, we propose a scalable multi-modal representation learning networks framework, which aims to learn optimal modality-specific projection matrices to project multi-modal features to a shared representation space. Specifically, weight guided modality-wise and row-sparsity driven feature-wise measures are considered to achieve adaptively hierarchical feature selection from the original data. Then, within the unified latent representation space, we employ hypergraph embedding to preserve the intricate high-order local geometric structures within the modality-specific high-dimensional spaces. Finally, we propose a proximal operator-inspired network architecture to resolve the optimization objectives, streamlining the process of feature auto-weighted selection and representation learning. The experimental results highlight the effectiveness and superiority of the proposed method, while online testing on out-of-sample data further demonstrates robust generalization. The code of the proposed method is publicly available at: https://github.com/ZihanFang11/SMMRL.
Keywords:
Multi-modal learning
Representation learning
Inductive models
Sparse regularization
Journal
IF:
13.9
Papers:
6.1K
Citations:
1.9W
Organization
Cited Papers
.png){kind=icon}
ShareSaveShareSaveShareSave
View more