# 📦 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](https://github.com/mayug/freeze-align), enabling further research into concept coverage and reducing computational requirements for modality alignment.
## 📄 Paper
**Title:** Harnessing Frozen Unimodal Encoders for Flexible Multimodal…
# 📦 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. The dataset is curated from LAION400M by performing a concept-balanced collection of captions using caption-image-prototype similarity. The 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 code and resources for curating this dataset are part of our code release.
## 📄 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](https://arxiv.org/abs/2409.…
