EUFCC-CIR: a Composed Image Retrieval Dataset for GLAM Collections

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Lluis Gomez

Published: 28 Sept 2024, Last Modified: 04 Mar 2025ECCV Workshop on Artificial Intelligence for Digital HumanitiesEveryoneCC BY 4.0
Abstract: The intersection of Artificial Intelligence and Digital Hu- manities enables researchers to explore cultural heritage collections with greater depth and scale. In this paper, we present EUFCC-CIR, a dataset designed for Composed Image Retrieval (CIR) within Galleries, Libraries, Archives, and Museums (GLAM) collections. Our dataset is built on top of the EUFCC-340K image labeling dataset and contains over 180K an- notated CIR triplets. Each triplet is composed of a multi-modal query (an input image plus a short text describing the desired attribute manip- ulations) and a set of relevant target images. The EUFCC-CIR dataset fills an existing gap in CIR-specific resources for Digital Humanities. We demonstrate the value of the EUFCC-CIR dataset by highlighting its unique qualities in comparison to other existing CIR datasets and eval- uating the performance of several zero-shot CIR baselines. The dataset is publicly available at https://github.com/cesc47/EUFCC-CIR