Unimodal Encoders

Unimodal encoders, which process single data modalities (e.g., text, images), are increasingly crucial for building efficient and effective multimodal models. Current research focuses on leveraging pre-trained unimodal encoders to create multimodal systems through techniques like projection layers, modular fusion frameworks, and conditional prompting, often aiming to minimize fine-tuning and computational cost. This work is significant because it allows researchers to build powerful multimodal systems by combining existing, well-understood unimodal components, leading to more data-efficient and computationally tractable solutions for various applications, including image-text retrieval and biomedical analysis.