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CausalVLR is a python open-source framework for causal relation discovery, causal inference that implements state-of-the-art causal learning algorithms for various visual-linguistic reasoning tasks, such as VQA, Image/Video Captioning, Model Generalization and Robustness, Medical Report Generation, etc.

📘Documentation | 🛠️Installation | 👀Model Zoo | 🆕Update News | 🚀Ongoing Projects | 🤔Reporting Issues

• 📄 Table of Contents
• 📚 Introduction
• 🚀 What's New
• 👨‍🏫 Get Started
• 👀 Model Zoo
• 🎫 License
• 🖊️ Citation
• 🙌 Contribution
• 🤝 Acknowledgement
• 🏗️ Projects in HCPLab

CausalVLR is a python open-source framework based on PyTorch for causal relation discovery, causal inference that implements state-of-the-art causal learning algorithms for various visual-linguistic reasoning tasks, detail see on Documentation.

❗ Note: The framework is actively being developed. Feedbacks (issues, suggestions, etc.) are highly encouraged.

• v0.0.2 was released in 8/19/2023
• Support CaCo-CoT for Faithful Reasoning task in LLMs

• v0.0.1 was released in 6/30/2023
• Support VLCI for Medical Report Generation task
• Support CAMDA (T-PAMI 2023) for Causality-Aware Medical Diagnosis task
• Support CMCIR (T-PAMI 2023) for Event-Level Visual Question Answering task
• Support VCSR (ACM MM 2023) for Video Question Answering task
• Support Robust Fine-tuning (CVPR 2023) for Model Generalization and Robustness

Please see Overview for the general introduction of CausalVLR.

For detailed user guides and advanced guides, please refer to our documentation, and here is the code structure of toolbox.

Please refer to Installation for installation instructions in documentation.

Briefly, to use CausalVLR, we could install it using pip:

pip install CausalVLR

For causal discovery, there are various running examples in the test directory.

For the implemented modules, we provide unit tests for the convenience of developing your own methods.

Please feel free to let us know if you have any recommendation regarding datasets with high-quality. We are grateful for any effort that benefits the development of causality community.

This project is released under the Apache 2.0 license.

If you find this project useful in your research, please consider cite:

@misc{liu2023causalvlr,
      title={CausalVLR: A Toolbox and Benchmark for Visual-Linguistic Causal Reasoning}, 
      author={Yang Liu and Weixing Chen and Guanbin Li and Liang Lin},
      year={2023},
      eprint={2306.17462},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Please feel free to open an issue if you find anything unexpected. We are always targeting to make our community better!

CausalVLR is an open-source project and We appreciate all the contributors who implement their methods or add new features and users who give valuable feedback. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to reimplement existing methods and develop their new models.

Causal Reasoning Meets Visual Representation Learning: A Prospective Study

Machine Intelligence Research (MIR) 2022
A Review paper for causal reasoning and visual representation learning

@article{
  liu2022causal,
  title={Causal Reasoning Meets Visual Representation Learning: A Prospective Study},
  author={Liu, Yang and Wei, Yu-Shen and Yan, Hong and Li, Guan-Bin and Lin, Liang},
  journal={Machine Intelligence Research},
  pages={1--27},
  year={2022},
  publisher={Springer}
  }

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