We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages scene graph structures to create 22M diverse reasoning questions, all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate language biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. An extensive analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3\%, offering ample opportunity for new research to explore. We strongly hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding for images and language.

GQA是一个旨在解决以往VQA数据集的缺陷问题的实际可视化推理和组成问题回答数据集，它使用场景图结构创建了2200万个不同的推理问题，伴随着功能程序共同表示它们的语义，其中包括新的可调平滑技术来缓解问题偏差。贯穿数据集的还有一套新的度量标准，可以评估基本的质量，例如一致性、基础和可信性。

GQA：一个适用于真实世界视觉推理和组合问答的新数据集