Увійти
ідея - Multi-modal Entity Alignment
Pseudo-Label Calibration Semi-supervised Multi-Modal Entity Alignment Study
PCMEA introduces a semi-supervised approach for multi-modal entity alignment, enhancing alignment quality through pseudo-label calibration and contrastive learning.
Semantic Consistency in Multi-Modal Entity Alignment: Dirichlet Energy Approach
Proposing a novel approach using Dirichlet energy to achieve semantic consistency in multi-modal entity alignment.
Semantic Consistency in Multi-Modal Entity Alignment: DESAlign Framework
Proposing the DESAlign framework to address semantic inconsistency in multi-modal entity alignment.
Pseudo-Label Calibration für semi-überwachte Multi-Modal Entity Alignment
Pseudo-Label Calibration verbessert Multi-Modal Entity Alignment in semi-überwachten Einstellungen.
Leveraging Intra-modal and Inter-modal Interactions for Effective Multi-Modal Entity Alignment
The proposed MIMEA framework effectively realizes multi-granular interaction mechanisms within and across modalities to enhance multi-modal knowledge representation and alignment.
Продукти | Ресурси
© 2024 by Linnk AI