通过文字看见：利用语言模型控制视觉检索质量
Seeing Through Words: Controlling Visual Retrieval Quality with Language Models
AI总结：
通过生成语言模型扩展短查询并控制图像质量，提升视觉检索效果和可控性
摘要：
文本到图像检索是视觉语言学习中的基本任务，但在现实场景中常常受到短且描述不充分的用户查询的挑战。此类查询通常只有一到两个词长，导致其语义模糊，容易在多样化的视觉解释中产生冲突，并缺乏对检索图像质量的明确控制。为了解决这些问题，我们提出了一种新的质量可控检索范式，该范式通过丰富短查询的上下文细节并融入明确的图像质量概念。我们的核心思想是利用生成语言模型作为查询完成函数，将不明确的查询扩展为描述性形式，以捕捉细粒度的视觉属性，如姿态、场景和审美。我们引入了一个通用框架，该框架将查询完成条件化于离散的质量等级上，这些等级源自相关性和审美评分模型，因此查询丰富不仅在语义上具有意义，而且具有质量意识。所得到的系统提供了三个关键优势：1）灵活性，它可以与任何预训练的视觉语言模型（VLMs）兼容，而无需修改；2）透明性，丰富查询可以由用户明确解释；3）可控性，使检索结果能够朝着用户偏好的质量等级引导。广泛的实验表明，我们提出的方法显著提高了检索结果，并提供了有效的质量控制，弥合了现代VLMs的表达能力与短用户查询的描述不充分之间的差距。我们的代码可在https://github.com/Jianglin954/QCQC上获得。
英文摘要：
Text-to-image retrieval is a fundamental task in vision-language learning, yet in real-world scenarios it is often challenged by short and underspecified user queries. Such queries are typically only one or two words long, rendering them semantically ambiguous, prone to collisions across diverse visual interpretations, and lacking explicit control over the quality of retrieved images. To address these issues, we propose a new paradigm of quality-controllable retrieval, which enriches short queries with contextual details while incorporating explicit notions of image quality. Our key idea is to leverage a generative language model as a query completion function, extending underspecified queries into descriptive forms that capture fine-grained visual attributes such as pose, scene, and aesthetics. We introduce a general framework that conditions query completion on discretized quality levels, derived from relevance and aesthetic scoring models, so that query enrichment is not only semantically meaningful but also quality-aware. The resulting system provides three key advantages: 1) flexibility, it is compatible with any pretrained vision-language model (VLMs) without modification; 2) transparency, enriched queries are explicitly interpretable by users; and 3) controllability, enabling retrieval results to be steered toward user-preferred quality levels. Extensive experiments demonstrate that our proposed approach significantly improves retrieval results and provides effective quality control, bridging the gap between the expressive capacity of modern VLMs and the underspecified nature of short user queries. Our code is available at https://github.com/Jianglin954/QCQC.
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。
