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license: mit
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<h1><a color="red" href="https://arxiv.org/pdf/2507.17343">Principled Multimodal Representation Learning (PMRL)</a></h1>

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
![License](https://img.shields.io/badge/Accepted-TPAMI'2026-purple)
[![License: MIT](https://img.shields.io/badge/Github-PMRL-black.svg)](https://github.com/Xiaohao-Liu/PMRL)

*A Novel Framework for Representation Learning Across Multiple Modalities*

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## ✨ Overview

![](img/top.png)

**Principled Multimodal Representation Learning (PMRL)** addresses the fundamental challenges in multimodal representation learning by proposing a novel framework that achieves simultaneous alignment of multiple modalities without anchor dependency.

### 💡 Our Solution

PMRL introduces a principled approach grounded in **theoretical insights**:

> **Key Insight**: Full alignment corresponds to a rank-1 Gram matrix

Our framework optimizes the dominant singular value of the representation matrix to align modalities along a shared leading direction.

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## 🎯 Key Features

🔄 **Simultaneous Multi-Modal Alignment**
- No predefined anchor modality required
- Unified representation space for all modalities

🧮 **Softmax-based Loss Function**
- Treats singular values as logits
- Prioritizes the largest singular value for stable optimization

🎯 **Instance-wise Contrastive Regularization**
- Maintains inter-instance separability
- Prevents representation collapse

⚡ **Distributed Training Support**
- Multi-GPU training capabilities
- Efficient data parallel processing

📊 **Comprehensive Evaluation**
- Extensive benchmarking across diverse tasks
- Quantitative and qualitative analysis tools

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## 🏗️ Architecture

![](img/framework.png)

The PMRL framework consists of three main components:

1. **🔧 Multi-Modal Encoder**: Processes different input modalities
2. **🎯 Singular Value Optimization**: Aligns representations via dominant singular value
3. **🔄 Principled Regularization**: Maintains instance-level discrimination


## Citation

If you find this work useful, please consider citing:

```bibtex
@article{liu2026principled,
  title={Principled multimodal representation learning},
  author={Liu, Xiaohao and Xia, Xiaobo and Ng, See-Kiong and Chua, Tat-Seng},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2026},
  publisher={IEEE}
}
```



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