Multimodal Prompting

Multimodal prompting leverages the power of multiple data modalities (e.g., text, images, video) to enhance the performance of machine learning models, particularly in few-shot and continual learning scenarios. Current research focuses on developing effective prompting strategies, often employing transformer-based architectures and contrastive learning methods, to guide models towards desired outputs by integrating modality-specific and cross-modal information. This approach is proving valuable across diverse applications, including medical image analysis, visual reasoning, and robotic control, by improving model accuracy and efficiency while reducing the need for extensive training data.