EUFCC-CIR: a Composed Image Retrieval Dataset for GLAM Collections
<|reference_start|>EUFCC-CIR: a Composed Image Retrieval Dataset for GLAM Collections: The intersection of Artificial Intelligence and Digital Humanities 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 annotated CIR triplets. Each triplet is composed of a multi-modal query (an input image plus a short text describing the desired attribute manipulations) 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 evaluating the performance of several zero-shot CIR baselines.<|reference_end|>
@article{net2024eufcc-cir:,
    title={EUFCC-CIR: a Composed Image Retrieval Dataset for GLAM Collections},
    author={Francesc Net and Lluis Gomez},
    journal={arXiv preprint arXiv:2410.01536},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01536},
    primaryClass={cs.CV}
}