Multimodal Search

Multimodal search aims to improve information retrieval by allowing users to query databases using multiple input modalities, such as text and images, mirroring how humans naturally search for information. Current research focuses on leveraging large multimodal models (LLMs) and retrieval-augmented generation (RAG) techniques, often incorporating specialized agents for tasks like query understanding and result summarization, to enhance search accuracy and personalization. This field is significant because it promises to revolutionize information access across diverse domains, from e-commerce and cultural heritage management to video and image retrieval, by enabling more intuitive and effective search experiences.