Datasets:

mayug
/

concept_coverage_laion_6m

license: cc-by-4.0
task_categories:
  - zero-shot-classification
  - text-to-image
  - image-to-text
language:
  - en
tags:
  - image-caption
  - high-concept-coverage
  - laion-subset
  - 6M
  - VLM
pretty_name: free-align-concept_covered_6M
size_categories:
  - 1M<n<10M

📦 Freeze-Align Dataset

The Freeze-Align Dataset (concept_coverage_laion_6m) is a curated collection of high-quality image-text pairs designed to facilitate efficient multimodal alignment using frozen unimodal encoders. This dataset supports the research presented in our CVPR 2025 paper, "Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment", enabling models to achieve CLIP-level performance with significantly reduced computational resources.

The dataset is curated from LAION-400M through a concept-balanced selection of captions, leveraging caption-to-image-prototype similarity to ensure diverse and semantically rich image-text pairs. The code and resources for curating this dataset are available in our GitHub repository, enabling further research into concept coverage and reducing computational requirements for modality alignment.

📄 Paper

Title: Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment
Authors: Mayug Maniparambil, Raiymbek Akshulakov, Yasser Abdelaziz Dahou Djilali, Sanath Narayan, Ankit Singh, Noel E. O'Connor
Conference: CVPR 2025
Paper: arXiv:2409.19425
Code: GitHub Repository

📊 Dataset Statistics

Total Samples: 6,000,000 image-text pairs
Source: Curated from LAION-400M using concept-balanced selection via caption-to-image-prototype similarity.
Image Resolution: Variable; standardized during preprocessing
Text Language: Primarily English
Data Format: Parquet files with fields: image_url, caption, embedding_vector, similarity_score
License: CC-BY 4.0

🧪 Usage

This dataset is intended for training and evaluating multimodal models that align visual and textual representations. It is particularly useful for research in:

Multimodal representation learning
Cross-modal retrieval
Zero-shot image classification
Efficient training with frozen encoders
Representational similarity studies

To load the dataset using the Hugging Face datasets library:

from datasets import load_dataset

dataset = load_dataset("mayug/concept_coverage_laion_6m")

📂 Dataset Structure

Each entry in the dataset includes:

image_url: URL to the image
caption: Associated textual description
similarity: Cosine similarity score between image and text embeddings
IMGNET_CLASS: One of 2754 ImageNet-derived classes the datapoint is assigned to
SCORE: Cosine similarity score indicating the datapoint's association with the assigned IMGNET_CLASS

📬 Citation

If you use this dataset in your research, please cite our paper:

@inproceedings{maniparambil2025harnessing,
  title={Harnessing Frozen Unimodal Encoders for Flexible Multimodal Alignment},
  author={Maniparambil, Mayug and Akshulakov, Raiymbek and Djilali, Yasser Abdelaziz Dahou and Narayan, Sanath and Singh, Ankit and O'Connor, Noel E},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2025}
}

For more details and updates, please visit our GitHub Repository.