Multimodal Data Fusion — Multi-Input Prediction Pipeline

I will build a multimodal data fusion model — combining structured tabular data, text, and image inputs for a prediction or classification task where each modality contributes complementary information that single-modality models miss. Multimodal fusion for prediction is most valuable when modalities carry genuinely complementary information — a medical diagnosis model that combines clinical notes (text), test results (tabular), and imaging (images) uses each modality's unique perspective on the patient's condition, outperforming any single-modality model because no single modality captures the complete clinical picture.

The build covers modality-specific encoder design, fusion architecture selection (early, late, or cross-attention fusion), training with modality dropout for robustness to missing modalities, evaluation against single-modality baselines, and an inference API accepting partial or complete modality inputs.