arXiv:2606.30355v1 Announce Type: cross Abstract: As real-world prediction systems often face missing modalities at inference, incomplete multimodal learning (IML) remains a practical challenge. While prior methods aim to learn representations robust to missing inputs, representations from incomplete modalities inevitably deviate from their full-modality counterparts due to missing evidence. To explicitly leverage these deviations, we propose MARS (Missingness-Aware Residual-guided Specialization), a mixture-of-experts framework that guides expert specialization based on how representations ar
The increasing deployment of AI systems in real-world scenarios highlights the persistent and practical challenge of dealing with incomplete multimodal data during inference.
This research addresses a fundamental limitation in multimodal AI, enabling more robust and reliable AI systems in environments where data collection is imperfect or intermittent.
New machine learning architectures, like MARS, are being developed to explicitly handle missing data, moving beyond simply robust representations to leveraging missingness itself.
- · AI developers
- · Industries relying on multimodal AI
- · Multimodal AI models
- · AI models vulnerable to incomplete data
- · Traditional data imputation methods
Improved performance and reliability of AI systems operating with noisy or partially available data.
Reduced data acquisition costs in some AI applications as systems can tolerate greater data incompleteness.
Acceleration of multimodal AI adoption in complex, real-world predictive applications where data streams are inherently unreliable.
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Read at arXiv cs.AI