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

• URL: http://arxiv.org/abs/2504.08739v1
• Date: Fri, 21 Mar 2025 05:44:15 GMT
• Title: Enhancing Product Search Interfaces with Sketch-Guided Diffusion and Language Agents
• Authors: Edward Sun,
• Abstract summary: Sketch-Search Agent is a novel framework that transforms the image search experience by integrating a multimodal language agent with freehand sketches as control signals for diffusion models.<n>Unlike existing methods, Sketch-Search Agent requires minimal setup, no additional training, and excels in sketch-based image retrieval and natural language interactions.<n>This interactive design empowers users to create sketches and receive tailored product suggestions, showcasing the potential of diffusion models in user-centric image retrieval.
• Score: 0.6961946145048322
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• SwiftSketch: A Diffusion Model for Image-to-Vector Sketch Generation [57.47730473674261]
  We introduce SwiftSketch, a model for image-conditioned vector sketch generation that can produce high-quality sketches in less than a second. SwiftSketch operates by progressively denoising stroke control points sampled from a Gaussian distribution. ControlSketch is a method that enhances SDS-based techniques by incorporating precise spatial control through a depth-aware ControlNet.
  arXiv  Detail & Related papers  (2025-02-12T18:57:12Z)
• Sketch2Code: Evaluating Vision-Language Models for Interactive Web Design Prototyping [55.98643055756135]
  We introduce Sketch2Code, a benchmark that evaluates state-of-the-art Vision Language Models (VLMs) on automating the conversion of rudimentary sketches into webpage prototypes. We analyze ten commercial and open-source models, showing that Sketch2Code is challenging for existing VLMs. A user study with UI/UX experts reveals a significant preference for proactive question-asking over passive feedback reception.
  arXiv  Detail & Related papers  (2024-10-21T17:39:49Z)
• FUSE-ing Language Models: Zero-Shot Adapter Discovery for Prompt Optimization Across Tokenizers [55.2480439325792]
  We propose FUSE, an approach to approximating an adapter layer that maps from one model's textual embedding space to another, even across different tokenizers. We show the efficacy of our approach via multi-objective optimization over vision-language and causal language models for image captioning and sentiment-based image captioning.
  arXiv  Detail & Related papers  (2024-08-09T02:16:37Z)
• Batch-Instructed Gradient for Prompt Evolution:Systematic Prompt Optimization for Enhanced Text-to-Image Synthesis [3.783530340696776]
  This study proposes a Multi-Agent framework to optimize input prompts for text-to-image generation models. A professional prompts database serves as a benchmark to guide the instruction modifier towards generating high-caliber prompts. Preliminary ablation studies highlight the effectiveness of various system components and suggest areas for future improvements.
  arXiv  Detail & Related papers  (2024-06-13T00:33:29Z)
• You'll Never Walk Alone: A Sketch and Text Duet for Fine-Grained Image Retrieval [120.49126407479717]
  We introduce a novel compositionality framework, effectively combining sketches and text using pre-trained CLIP models. Our system extends to novel applications in composed image retrieval, domain transfer, and fine-grained generation.
  arXiv  Detail & Related papers  (2024-03-12T00:27:18Z)
• Text-to-Image Diffusion Models are Great Sketch-Photo Matchmakers [120.49126407479717]
  This paper explores text-to-image diffusion models for Zero-Shot Sketch-based Image Retrieval (ZS-SBIR) We highlight a pivotal discovery: the capacity of text-to-image diffusion models to seamlessly bridge the gap between sketches and photos.
  arXiv  Detail & Related papers  (2024-03-12T00:02:03Z)
• Large Language Models for Captioning and Retrieving Remote Sensing Images [4.499596985198142]
  RS-CapRet is a Vision and Language method for remote sensing tasks. It can generate descriptions for remote sensing images and retrieve images from textual descriptions.
  arXiv  Detail & Related papers  (2024-02-09T15:31:01Z)
• Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation [72.6168579583414]
  CompAgent is a training-free approach for compositional text-to-image generation with a large language model (LLM) agent as its core. Our approach achieves more than 10% improvement on T2I-CompBench, a comprehensive benchmark for open-world compositional T2I generation.
  arXiv  Detail & Related papers  (2024-01-28T16:18:39Z)
• The Contemporary Art of Image Search: Iterative User Intent Expansion via Vision-Language Model [4.531548217880843]
  We introduce an innovative user intent expansion framework for image search. Our framework leverages visual-language models to parse and compose multi-modal user inputs. The proposed framework significantly improves users' image search experience.
  arXiv  Detail & Related papers  (2023-12-04T06:14:25Z)
• Reference-based Image Composition with Sketch via Structure-aware Diffusion Model [38.1193912666578]
  We introduce a multi-input-conditioned image composition model that incorporates a sketch as a novel modal, alongside a reference image. Thanks to the edge-level controllability using sketches, our method enables a user to edit or complete an image sub-part. Our framework fine-tunes a pre-trained diffusion model to complete missing regions using the reference image while maintaining sketch guidance.
  arXiv  Detail & Related papers  (2023-03-31T06:12:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.