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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>
[](https://opensource.org/licenses/MIT)
[](https://github.com/Xiaohao-Liu/PMRL)
*A Novel Framework for Representation Learning Across Multiple Modalities*
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## ✨ Overview
**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
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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