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

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🎨 EUFCC-CIR: A Composed Image Retrieval Dataset for GLAM Collections

Authors: Francesc Net, Lluis Gomez
Institution: Computer Vision Center, Universitat Autònoma de Barcelona


🏛️ Introduction

The EUFCC-CIR dataset is a cutting-edge resource designed to enable Composed Image Retrieval (CIR) within Galleries, Libraries, Archives, and Museums (GLAM) collections. It builds upon the EUFCC-340K image labeling dataset, providing over 180K meticulously annotated CIR triplets. Each triplet comprises:

  1. A query image 🖼️.
  2. A brief text description 📝 that specifies a desired modification.
  3. A target image 🎯 that fulfills the query.

This dataset is particularly beneficial for researchers in digital humanities, allowing them to retrieve and interact with cultural heritage items in new and engaging ways.

🔗 Paper link: EUFCC-CIR Paper
📂 Related dataset: EUFCC-340K


📁 Dataset Structure

The dataset is organized within the data/ folder, consisting of the following key files:

  • db_processed.txt: 📄 A list of images, each assigned to a partition (train, validation, test).
  • cir_db.csv: 🗃️ Contains detailed information about image-text pairs and their corresponding target images.

🗂️ CSV Columns Breakdown

🔢 Column 📖 Description
id1 🆔 Identifier for the first image in the pair.
id2 🆔 Identifier for the second image in the pair.
materials_1 🏗️ Materials in the first image. Specifies the substances or materials used in the first object.
ObjectTypes_1 🏷️ Object types in the first image. Defines the category or type of object represented.
materials_2 🏗️ Materials in the second image. Specifies the substances or materials used in the second object.
ObjectTypes_2 🏷️ Object types in the second image. Defines the category or type of object represented.
element_to_change 🔄 The element (materials or object type) that must change between the two images.
element_changed 🔄 The element (materials or object type) that was changed between the two images.
partition 📊 Denotes the dataset split (train, validation, test) that this row belongs to.
query 🔍 Describes the retrieval task, indicating how the transformation from id1 to id2 should occur.

Note: Each row in the CSV represents a relationship between two images (id1 and id2), where an element in the first image is altered to generate the second image.

🌟 Example

Below is an example visualization of how an image transformation is represented in this dataset:

Example Structure

⚙️ Installation & Usage

To get started, clone the repository and download the necessary files from the EUFCC-340K dataset:

git clone https://github.com/your-username/EUFCC-CIR.git 
cd EUFCC-CIR/data
# Download dataset files (from EUFCC-340K repo => link in the beginning)

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