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-# 🌟 Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation
-# NeurIPS 2025 
-> [Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation](https://arxiv.org/pdf/2505.14705?).<br>
-> [Xin Zhang](https://zhangxin-xd.github.io/), Ziruo Zhang, [Jiawei Du](https://scholar.google.com/citations?user=WrJKEzEAAAAJ&hl=zh-CN), [Zuozhu Liu](https://person.zju.edu.cn/en/lzz), [Joey Tianyi Zhou](https://joeyzhouty.github.io/) <br>
-> Agency for Science, Technology, and Research (ASTAR), Singapore <br>
-> National University of Singapore, Singapore <br>
-> Zhejiang University, China <br>
-## 📖 Introduction
-<p align="center">
-  <img src="imgs/problem.png" alt="problem" title="problem" width="700">
-</p>
-
-<p align="justify">
-  <strong> Multimodal embedding distributions across various distillation methods </strong>:
-  We extract image and text embeddings from a finetuned CLIP and project them into a shared representation space using DOSNES. 
-  Red triangles and blue circles denote image and text embeddings, respectively. 
-  Left: Embeddings from randomly sampled data in the original dataset exhibit a well-spread and modality-aligned distribution. 
-  Middle: The distilled dataset generated by a sota MDD method (LoRS) leads to Modality Collapse, where image and text embeddings are poorly aligned and concentrated in distinct regions. 
-  Right: Our method effectively mitigates modality collapse, yielding a distribution that better preserves cross-modal alignment and exhibits greater representational diversity.
-</p>
-
-## ⚙️ Installation
-
-To get started, follow these instructions to set up the environment and install dependencies.
-
-1. **Clone this repository**:
-    ```bash
-    git clone https://github.com/zhangxin-xd/RepBlend.git
-    cd RepBlend
-    ```
-
-2. **Install required packages**:
-    ```
-    conda create -n RepBlend python=3.10
-    conda activate RepBlend
-    pip install -r requirements.txt
-    ```
----
-
-## 🚀 Usage
-
-Here’s how to use RepBlend for Multimodal Dataset Distillation:
-
-First, download the pretrained weights and datasets and place them into their respective folders.
-### Pretrained Weights
-The checkpoints for all experimental networks are available from their respective official repositories. For convenience, we have also provided them together [🤗 here](https://huggingface.co/xinxin66/RepBlend).
-Once downloaded, put them in `distill_utils/checkpoints/`.
-
-### Experimental Datasets
-The dataset hase been validated on various benchmarks, you can download from  their respective links. Once downloaded, put them in `distill_utils/data/`.
-| datasets | links| 
-|-----|-----|
-| Flickr30K | [images](https://www.kaggle.com/datasets/hsankesara/flickr-image-dataset), [🤗 annotations](https://huggingface.co/xinxin66/RepBlend/)|
-| COCO | [images](https://cocodataset.org/#download), [🤗 annotations](https://huggingface.co/xinxin66/RepBlend) |
-|LLaVA-cc3m|[images](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md), [🤗 annotations](https://huggingface.co/xinxin66/RepBlend)|
-
-### Generate Expert Trajectories
-You can generate expert trajectories by running the `scripts/buffer.sh`, or alternatively, download our [pre-generated trajectories](🤗 https://huggingface.co/xinxin66/RepBlend) for faster reproduction.
-```
-bash scripts/buffer.sh
-```
-### Distill Multimodal Dataset
-You can distill multimodal datasets with RepBlend by running `scripts/distill_coco_repblend.sh` and `scripts/distill_flickr_repblend.sh`.
-```
-bash scripts/distill_coco_repblend.sh
-bash scripts/distill_flickr_repblend.sh
-```
-
-## 📊 Results
-
-Our experiments demonstrate the effectiveness of the proposed approach across various benchmarks. 
-<div style="display: flex; justify-content: center; align-items: center;">
-    <img src="imgs/results 1.png" alt="Results 1" width="800"/>
-</div>
-<br>
-<div style="display: flex; justify-content: center; align-items: center;">
-    <img src="imgs/table 1.png" alt="table 1" width="400"/>
-    <img src="imgs/table 2.png" alt="table 2" width="400"/>
-</div>
-
-For detailed experimental results and further analysis, please refer to the full paper.
-
----
-
-## 📑 Citation
-
-If you find this code useful in your research, please consider citing our work:
-
-```bibtex
-@inproceedings{RepBlend2025neurips,
-    title={Beyond Modality Collapse: Representations Blending for Multimodal Dataset Distillation},
-    author={Zhang, Xin and Zhang, Ziruo, and Du, Jiawei and Liu, Zuozhu and Zhou, Joey Tianyi},
-    booktitle={Adv. Neural Inf. Process. Syst. (NeurIPS)},
-    year={2025}
-}
-```
----
-## 🎉 Reference
-Our code has referred to previous works:
-- [LoRS: Low-Rank Similarity Mining](https://github.com/silicx/LoRS_Distill)
-- [Vision-Language Dataset Distillation](https://github.com/princetonvisualai/multimodal_dataset_distillation)
-- [Scaling Up Dataset Distillation to ImageNet-1K with Constant Memory (TESLA)](https://github.com/justincui03/tesla)
-