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README.md

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-license: mit
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+---
+license: mit
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+<div align="center">
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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)
+
+*A Novel Framework for Representation Learning Across Multiple Modalities*
+
+</div>
+
+---
+
+## ✨ 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.
+
+---
+
+## 🎯 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
+
+---
+
+## 🏗️ 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}
+}
+```
+
+
+
+<div align="center">
+
+
+**[🔝 Back to Top](#-principled-multimodal-representation-learning-pmrl)**
+
+</div>
+