Enhancing Product Search Interfaces with Sketch-Guided Diffusion and Language Agents

Abstract: The rapid progress in diffusion models, transformers, and language agents has unlocked new possibilities, yet their potential in user interfaces and commercial applications remains underexplored. We present Sketch-Search Agent, a novel framework that transforms the image search experience by integrating a multimodal language agent with freehand sketches as control signals for diffusion models. Using the T2I-Adapter, Sketch-Search Agent combines sketches and text prompts to generate high-quality query images, encoded via a CLIP image encoder for efficient matching against an image corpus. Unlike existing methods, Sketch-Search Agent requires minimal setup, no additional training, and excels in sketch-based image retrieval and natural language interactions. The multimodal agent enhances user experience by dynamically retaining preferences, ranking results, and refining queries for personalized recommendations. This interactive design empowers users to create sketches and receive tailored product suggestions, showcasing the potential of diffusion models in user-centric image retrieval. Experiments confirm Sketch-Search Agent's high accuracy in delivering relevant product search results.