cross-modal-learning

The study investigates cross-modal generalization in vision-language models for grammatical number agreement, demonstrating that these models can abstract beyond surface co-occurrence using visual cues.

This paper proposes CMF-ELN, a cross-modal fusion end-to-end learning network for cold-start drug-drug interaction prediction, using multi-modal knowledge graphs and a four-channel graph autoencoder to improve similarity modeling and interpretability.

Introduces CGM-JEPA, a self-supervised pretraining framework for continuous glucose monitor data that improves cross-modal and cross-cohort performance through masked latent prediction and distributional objectives.