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

arXiv 2410.01536 ↗First posted 2 Oct 2024cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 2 Oct 2024.

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13 of 20 references.

References · 13

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
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  5. 2023Pic2Word: Mapping Pictures to Words for Zero-shot Composed Image Retrievalcited by 155
  6. 2021Probabilistic Embeddings for Cross-Modal Retrievalcited by 150
  7. 2017Automatic Spatially-aware Fashion Concept Discoverycited by 132
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  10. 2023COLA: A Benchmark for Compositional Text-to-image Retrievalcited by 53
  11. 2022Probabilistic Compositional Embeddings for Multimodal Image Retrievalcited by 29
  12. 2022UIGR: Unified Interactive Garment Retrievalcited by 6
1 more
  1. 2024EUFCC-340K: A Faceted Hierarchical Dataset for Metadata Annotation in GLAM Collectionscited by 1

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