Efficient Multimodal Fusion

Efficient multimodal fusion aims to combine information from different data sources (e.g., images, text, sensor data) to improve the performance of machine learning models while minimizing computational cost and data requirements. Current research emphasizes developing lightweight architectures, such as vision transformers and prompt-based methods, that leverage pre-trained unimodal models and data augmentation techniques to achieve competitive results with significantly reduced training resources. This focus on efficiency is crucial for deploying multimodal models in resource-constrained environments and expanding their applicability across diverse fields, including medical diagnosis, remote sensing, and information retrieval.