Text-to-image search allows users to find relevant images by inputting a textual description. For example, typing "red shoes with white soles" retrieves images that match this description. The system translates the text query into a vector representation and compares it with precomputed image embeddings to find the closest matches.
This search method relies on multimodal models like CLIP, which can understand the relationship between text and images by mapping both into a shared vector space. Applications include e-commerce, where users can search for products without knowing exact keywords, and creative tools that generate or retrieve visuals based on descriptive input.
Text-to-image search enhances accessibility and efficiency, making it easier to locate specific content without relying on detailed metadata or manual tagging.