Modality Completion

Modality completion addresses the challenge of reconstructing missing data in multi-modal datasets, aiming to improve the performance of AI systems that rely on diverse data sources. Current research focuses on developing methods that leverage pre-trained models and deep learning architectures, such as deep belief networks and contrastive learning frameworks, to effectively infer missing modalities, often guided by existing data or prototypes. This work is significant because it enables more robust and accurate AI applications across various domains, including person re-identification, stock market forecasting, and medical imaging, where incomplete data is a common problem.