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

Abstract

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 resulting variation via a reliability-gated residual mechanism. Experiments on multimodal auditory attention decoding and emotion recognition showed that CoMA-DiT consistently outperformed 20 representative baselines, achieving absolute gains of 4.28% and 6.70% in accuracy and macro-F1 over the no-augmentation baseline, respectively. Extensive ablation, sensitivity, visualization, and interpretability analyses further demonstrated its robustness, generalizability, and ability to capture functionally relevant cross-modal interactions. These findings support a broader view of multimodal learning: Paired modalities can serve not only as inputs for fusion but also as supervision sources that augment one another.

Authors 6

Bohan Fang, Dongrui Wu, Hongbin Wang, Tianwang Jia, Xingyi He, Ziwei Wang

Specification

Official page: arxiv.org/abs/2609.11341
Source:arXiv (Atom API + RSS)T1observed 56 min agohigh
Arxiv announce type: new
Source:arXiv (Atom API + RSS)T1observed 56 min agohigh
arXiv id: 2609.11341
Source:arXiv (Atom API + RSS)T1observed 56 min agohigh
Categories: cs.AI
Source:arXiv (Atom API + RSS)T1observed 56 min agohigh
PDF: arxiv.org/pdf/2609.11341
Source:arXiv (Atom API + RSS)T1observed 56 min agohigh
Primary category: cs.AI
Source:arXiv (Atom API + RSS)T1observed 56 min agohigh
Published: 12 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 56 min agohigh

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T19

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56 min ago

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Authors: Ziwei Wang, Xingyi He, Hongbin Wang

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9 claims · 9 properties

Official pageofficial_url1

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https://arxiv.org/abs/2609.11341	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Abstractabstract1

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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 resulting variation via a reliability-gated residual mechanism. Experiments on multimodal auditory attention decoding and emotion recognition showed that CoMA-DiT consistently outperformed 20 representative baselines, achieving absolute gains of 4.28% and 6.70% in accuracy and macro-F1 over the no-augmentation baseline, respectively. Extensive ablation, sensitivity, visualization, and interpretability analyses further demonstrated its robustness, generalizability, and ability to capture functionally relevant cross-modal interactions. These findings support a broader view of multimodal learning: Paired modalities can serve not only as inputs for fusion but also as supervision sources that augment one another.	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Arxiv announce typearxiv_announce_type1

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new	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

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2609.11341	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

Authorsauthors1

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Bohan Fang, Dongrui Wu, Hongbin Wang, Tianwang Jia, Xingyi He, Ziwei Wang	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

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cs.AI	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

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https://arxiv.org/pdf/2609.11341	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

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cs.AI	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

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12 Sept 2026	12 Sept 2026 04:12 UTC → current	current	arXiv (Atom API + RSS)T1	high	deterministic

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12 Sept 2026
New paperPaperExploring Diffusion Transformers for Cross-Modal Augmentation in Multimodal Brain State Decoding
New paper: Exploring Diffusion Transformers for Cross-Modal Augmentation in Multimodal Brain State Decoding
arxiv
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arXiv (Atom API + RSS)	rss.arxiv.org/rss/cs.AI	feed	T1· Official	56 min ago	2

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