The CLEVR dataset has been used extensively in language grounded visual reasoning in Machine Learning (ML) and Natural Language Processing (NLP) domains. We present a graph parser library for CLEVR, that provides functionalities for object-centric attributes and relationships extraction, and construction of structural graph representations for dual modalities. Structural order-invariant representations enable geometric learning and can aid in downstream tasks like language grounding to vision, robotics, compositionality, interpretability, and computational grammar construction. We provide three extensible main components - parser, embedder, and visualizer that can be tailored to suit specific learning setups. We also provide out-of-the-box functionality for seamless integration with popular deep graph neural network (GNN) libraries. Additionally, we discuss downstream usage and applications of the library, and how it accelerates research for the NLP research community.

本研究介绍了一个用于CLEVR的图形解析器库，可提取基于对象的属性和关系，并为双模态构建结构图表示。结构无序表示使几何学习更容易，可辅助下游任务，例如语言向视觉的基础，机器人学，组合性，可解释性和计算语法构建。我们提供了三个可扩展的主要组件-解析器，嵌入器和可视化器，旨在满足特定学习设置的需求。我们还提供了开箱即用的功能，可无缝集成流行的深度图形神经网络（GNN）库。此外，我们还讨论了库的下游用途和应用，以及它如何加速自然语言处理研究社区的研究。

CLEVR Parser：面向语言图像环境的几何学习图解析库