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ImageSet2Text: Describing Sets of Images through Text by Riccio, Piera; Galati, Francesco; Schweighofer, Kajetan; Garcia, Noa; Oliver, Nuria is a scholarly article available to read on EtoBox.

What is ImageSet2Text: Describing Sets of Images through Text about?

We introduce ImageSet2Text, a novel approach that leverages vision-language foundation models to automatically create natural language descriptions of image sets. Inspired by concept bottleneck models (CBMs) and based on visual-question answering (VQA) chains, ImageSet2Text iteratively extracts key concepts from image subsets, encodes them into a structured graph, and refines insights using an external knowledge graph and CLIP-based validation. This iterative process enhances interpretability and enables accurate and detailed set-level summarization. Through extensive experiments, we evaluate ImageSet2Text's descriptions on accuracy, completeness, readability and overall quality, benchmarking it against existing vision-language models and introducing new datasets for large-scale group image captioning.

Author

Riccio, Piera; Galati, Francesco; Schweighofer, Kajetan; Garcia, Noa; Oliver, Nuria

Published

2025

Language

EN