In the era of large-scale visual data, understanding collections of images is a challenging yet important task. To this end, we introduce ImageSet2Text, a novel method to automatically generate natural language descriptions of image sets. Based on large language models, visual-question answering chains, an external lexical graph, and CLIP-based verification, ImageSet2Text iteratively extracts key concepts from image subsets and organizes them into a structured concept graph. We conduct extensive experiments evaluating the quality of the generated descriptions in terms of accuracy, completeness, and user satisfaction. We also examine the method's behavior through ablation studies, scalability assessments, and failure analyses. Results demonstrate that ImageSet2Text combines data-driven AI and symbolic representations to reliably summarize large image collections for a wide range of applications.

翻译：在大规模视觉数据时代，理解图像集合是一项具有挑战性且重要的任务。为此，我们提出了ImageSet2Text，一种自动生成图像集合自然语言描述的新方法。该方法基于大型语言模型、视觉问答链、外部词汇图以及基于CLIP的验证机制，通过迭代地从图像子集中提取关键概念，并将其组织为结构化的概念图。我们进行了广泛的实验，从准确性、完整性和用户满意度等方面评估生成描述的质量。同时，通过消融研究、可扩展性评估和失败案例分析，深入探讨了该方法的行为特性。结果表明，ImageSet2Text结合了数据驱动的人工智能与符号表示，能够可靠地为广泛的应用场景总结大规模图像集合。