Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.
Garomsa, Anna Zapaishchykova, Tafadzwa L
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MICViT outperforms CNN and transformer baselines on brain age prediction from multimodal 3D MRI by combining modality-specific and cross-modal local/global attention across three heterogeneous datasets.
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Empirical comparison of graded MRI preprocessing levels for MAE and JEPA pretraining on brain scans shows moderate levels (P2) are often sufficient, with limited additional utility from stronger preprocessing on downstream tasks.
Bayesian meta-learner predicts individualized Alzheimer's disease progression distributions from MRI and trajectories, competitive on ADNI data and less overconfident for long-term scores than deterministic versions.
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Hidden in the Multiplicative Interaction: Uncovering Fragility in Multimodal Contrastive Learning
Multimodal contrastive learning using multilinear products is fragile to single bad modalities, and a gated version improves top-1 retrieval accuracy on synthetic and real trimodal data.