The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model

Understand

Image search is an essential and user-friendly method to explore vast galleries of digital images.

• However, existing image search methods heavily rely on proximity measurements like tag matching or image similarity, requiring precise user inputs for satisfactory results.
• To meet the growing demand for a contemporary image search engine that enables accurate comprehension of users' search intentions, we introduce an innovative user intent expansion framework.
• Our framework leverages visual-language models to parse and compose multi-modal user inputs to provide more accurate and satisfying results.