SOURCE-LINKED INTELLIGENCE

Exploring Diffusion Transformers for Cross-Modal Augmentation in Multimodal Brain State Decoding

arXiv · AI, language, vision and robotics · article · Sep 10, 2026 · UTC

Multimodal brain state decoding has largely focused on fusing paired modalities for prediction, but has rarely explored how their correspondence can be further exploited to enrich training data and improve multimodal representation learning. To address this gap, we propose CoMA-DiT, a bidirectional cross-modal Diffusion Transformer for latent augmentation that treats paired modalities as sources of mutual generative supervision rather than merely as inputs to be fused. CoMA-DiT conditions velocity prediction on the paired modality through cross-modal attention and adaptively injects the result

Read original source ↗ Open in workspace

recordType

paper

region

Global