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license: mit
Principled Multimodal Representation Learning (PMRL)
A Novel Framework for Representation Learning Across Multiple Modalities
✨ 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.
🎯 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
The PMRL framework consists of three main components:
- 🔧 Multi-Modal Encoder: Processes different input modalities
- 🎯 Singular Value Optimization: Aligns representations via dominant singular value
- 🔄 Principled Regularization: Maintains instance-level discrimination
Citation
If you find this work useful, please consider citing:
@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}
}