TIGeR: Unifying Text-to-Image Generation and Retrieval with Large Multimodal Models

Understand

How humans can effectively and efficiently acquire images has always been a perennial question.

• A classic solution is text-to-image retrieval from an existing database; however, the limited database typically lacks creativity.
• By contrast, recent breakthroughs in text-to-image generation have made it possible to produce attractive and counterfactual visual content, but it faces challenges in synthesizing knowledge-intensive images.
• In this work, we rethink the relationship between text-to-image generation and retrieval, proposing a unified framework for both tasks with one single Large Multimodal Model (LMM).