We present CausalVLR (Causal Visual-Linguistic Reasoning), an open-source toolbox containing a rich set of state-of-the-art causal relation discovery and causal inference methods for various visual-linguistic reasoning tasks, such as VQA, image/video captioning, medical report generation, model generalization and robustness, etc. These methods have been included in the toolbox with PyTorch implementations under NVIDIA computing system. It not only includes training and inference codes, but also provides model weights. We believe this toolbox is by far the most complete visual-linguitic causal reasoning toolbox. We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new causal reasoning methods. Code and models are available at https://github.com/HCPLab-SYSU/Causal-VLReasoning. The project is under active development by HCP-Lab's contributors and we will keep this document updated.

翻译：我们推出CausalVLR（因果视觉-语言推理），这是一个开源工具包，集成了丰富的最先进因果关联发现与因果推断方法，适用于各类视觉-语言推理任务，如VQA、图像/视频描述生成、医学报告生成、模型泛化与鲁棒性等。这些方法已在NVIDIA计算系统下通过PyTorch实现集成于工具包中。工具包不仅提供训练与推理代码，还附带模型权重。我们相信该工具包是目前最完整的视觉-语言因果推理工具包。期望该工具包与基准能够通过提供灵活的工具集，帮助日益壮大的研究社区重新实现现有方法并开发其自身的新型因果推理方法。代码与模型可通过https://github.com/HCPLab-SYSU/Causal-VLReasoning获取。该项目由HCP-Lab贡献者持续积极开发，我们将保持文档更新。