Multimodal Classification

Multimodal classification studies how to combine different kinds of evidence—for example, medical images with clinical records, or meme images with text—to predict categories. Common approaches include feature or decision fusion, cross-attention and contrastive alignment, mixture-of-experts models, and large vision-language models; benchmarks range from medical diagnosis and sentiment analysis to document, time-series, and content moderation tasks. Current work also addresses missing or unreliable inputs through modality dropout, imputation, adaptive routing, and selective acquisition, while evaluating fairness, calibration, and interpretability. A central tension is that adding a modality can improve predictions when it contributes complementary information, but can also hurt when it is noisy, redundant, or unavailable at deployment.