Shadi Albarqouni

K-MaT is a prompt-learning framework that factorizes prompts, anchors them to clinical text descriptions, and aligns the low-end prompt manifold to the visually-grounded high-end space using Fused Gromov-Wasserstein optimal transport, transferring decision structures to low-end modalities without requiring low-end training images; across four cross-modal benchmarks including dermoscopy, mammography to ultrasound, and CT to chest X-ray it achieves state-of-the-art results, raising the average harmonic mean of accuracy to 44.1% from BiomedCoOp's 42.0% with macro-F1 of 36.2%, and on the challenging breast imaging task it mitigates the catastrophic forgetting seen in CoOp, which drops to 27.0% accuracy on the low-end.

K-MaT is a prompt-learning framework that factorizes prompts, anchors them to clinical text descriptions, and aligns the low-end prompt manifold to the visually-grounded high-end space using Fused Gromov-Wasserstein optimal transport, transferring decision structures to low-end modalities without requiring low-end training images; across four cross-modal benchmarks including dermoscopy, mammography to ultrasound, and CT to chest X-ray it achieves state-of-the-art results, raising the average harmonic mean of accuracy to 44.1% from BiomedCoOp's 42.0% with macro-F1 of 36.2%, and on the challenging breast imaging task it mitigates the catastrophic forgetting seen in CoOp, which drops to 27.0% accuracy on the low-end.