ImageSet2Text: Describing Sets of Images through Text
This addresses the challenge of understanding collections of images for applications in visual data analysis, though it appears incremental as it builds on existing components like LLMs and CLIP.
The paper tackles the problem of automatically generating natural language descriptions for sets of images by introducing ImageSet2Text, which combines large language models, visual-question answering chains, an external lexical graph, and CLIP-based verification to extract and organize key concepts into a structured graph. Results show the method reliably summarizes large image collections for various applications, with evaluations covering accuracy, completeness, and user satisfaction.
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.