Intermediate Fusion

Intermediate fusion in multimodal deep learning combines information from different data sources (e.g., images, text, sensor data) at intermediate stages of a model's processing, rather than at the beginning or end. Current research focuses on improving the efficiency and robustness of these methods across diverse applications, including biomedical image analysis, autonomous driving, and object tracking, often employing vision transformers (ViTs) and prompt-based fine-tuning techniques to address challenges like data scarcity and computational cost. This approach offers significant advantages in creating more accurate and efficient models by leveraging the complementary strengths of multiple data modalities, leading to advancements in various fields ranging from healthcare to robotics.