README.md

Calibrated Multimodal Representation Learning with Missing Modalities

Multimodal representation learning under partial-modality settings

✨ Overview

CalMRL is a multimodal representation learning framework designed for alignment calibration when some modalities are missing. CalMRL combines two complementary goals:

• Cross-modal alignment for robust shared representations
• Missing-modality calibration through posterior inference and learned generative parameters

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🎯 Key Features

🔄 Partial-Modality Learning

• Handles missing video, audio, text, or subtitle signals
• Supports posterior-based feature completion with learned modality-specific parameters

🎯 Multimodal Retrieval

• Joint training over text-video, text-audio, text-video-audio, and subtitle-aware setups
• Config-driven recipes for pretraining, finetuning, and evaluation

🧠 Feature Calibration

• Uses latent posterior inference for modality completion
• Includes a warmup pipeline to estimate W, mu, and log_sigma

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🏗️ Architecture

The current codebase is organized around three main stages:

1. 🔧 Multimodal Encoding: Video, audio, text, and subtitle features are extracted with VAST-style encoders.
2. 🧮 Representation Calibration: Shared embeddings are aligned while latent posterior inference estimates missing information.
3. 🔄 Downstream Evaluation: Retrieval and other tasks are executed through a unified config-driven pipeline.

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Citation

If this project is useful for your research, you can cite the work as:

@article{liu2025calibrated,
  title={Calibrated Multimodal Representation Learning with Missing Modalities},
  author={Liu, Xiaohao and Xia, Xiaobo and Wei, Jiaheng and Yang, Shuo and Su, Xiu and Ng, See-Kiong and Chua, Tat-Seng},
  journal={arXiv preprint arXiv:2511.12034},
  year={2025}
}

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