This cluster of papers focuses on the development and application of knowledge base graph embedding techniques for visual question answering models. It explores methods such as multi-scale relational networks, semantic representation, and deep learning for tasks including image classification, endoscope image mosaic, and haze prediction. The research also delves into spatial statistics and semantic reasoning to improve visual reasoning and feature extraction in the context of visual question answering.