GenIR：面向心理意象检索的生成式视觉反馈
GenIR: Generative Visual Feedback for Mental Image Retrieval
AI总结：
本研究提出心理意象检索（MIR）新任务，推出基于扩散模型的生成式多轮检索范式GenIR，通过生成可视化反馈辅助用户优化查询，同时构建了全自动数据集生成流水线，实验显示其性能显著优于现有交互式方法。
摘要：
视觉语言模型（VLM）已在文本到图像检索基准上展现出优异性能，但将这一成功落地到现实应用仍存在挑战。实际场景中，人类的搜索行为很少是单次完成的，反而通常是由脑海中的线索引导的多轮过程——即从模糊回忆到目标图像清晰心理表征的心理意象。受这一研究缺口驱动，我们研究了心理意象检索（Mental Image Retrieval, MIR）任务，该任务针对一个现实但尚未被充分探索的场景：用户通过与图像搜索引擎的多轮交互，优化对脑海中构想图像的搜索。交互式检索成功的核心在于机器能否为用户提供清晰、可执行的反馈，但现有方法依赖间接或抽象的文字反馈，这类反馈可能存在歧义、产生误导，或无法有效帮助用户优化查询。为解决这一问题，我们提出GenIR——一种生成式多轮检索范式，它利用基于扩散的图像生成技术，在每一轮明确具象化AI系统的理解结果。这些合成的视觉表征提供了清晰、可解释的反馈，让用户能够直观且有效地优化查询。我们还进一步提出了一套全自动流水线，用于生成高质量的多轮MIR数据集。实验结果表明，在MIR场景下，GenIR的性能显著优于现有交互式方法。本工作确立了一项新任务，配套提供了数据集和有效的生成式检索方法，为该方向的后续研究奠定了基础。
英文摘要：
Vision-language models (VLMs) have shown strong performance on text-to-image retrieval benchmarks. However, bridging this success to real-world applications remains a challenge. In practice, human search behavior is rarely a one-shot action. Instead, it is often a multi-round process guided by clues in mind. That is, a mental image ranging from vague recollections to vivid mental representations of the target image. Motivated by this gap, we study the task of Mental Image Retrieval (MIR), which targets the realistic yet underexplored setting where users refine their search for a mentally envisioned image through multi-round interactions with an image search engine. Central to successful interactive retrieval is the capability of machines to provide users with clear, actionable feedback; however, existing methods rely on indirect or abstract verbal feedback, which can be ambiguous, misleading, or ineffective for users to refine the query. To overcome this, we propose GenIR, a generative multi-round retrieval paradigm leveraging diffusion-based image generation to explicitly reify the AI system's understanding at each round. These synthetic visual representations provide clear, interpretable feedback, enabling users to refine their queries intuitively and effectively. We further introduce a fully automated pipeline to generate a high-quality multi-round MIR dataset. Experimental results demonstrate that GenIR significantly outperforms existing interactive methods in the MIR scenario. This work establishes a new task with a dataset and an effective generative retrieval method, providing a foundation for future research in this direction
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
