Knowledge-based Vision Question Answering (KB-VQA) extends general Vision Question Answering (VQA) by not only requiring the understanding of visual and textual inputs but also extensive range of knowledge, enabling significant advancements across various real-world applications. KB-VQA introduces unique challenges, including the alignment of heterogeneous information from diverse modalities and sources, the retrieval of relevant knowledge from noisy or large-scale repositories, and the execution of complex reasoning to infer answers from the combined context. With the advancement of Large Language Models (LLMs), KB-VQA systems have also undergone a notable transformation, where LLMs serve as powerful knowledge repositories, retrieval-augmented generators and strong reasoners. Despite substantial progress, no comprehensive survey currently exists that systematically organizes and reviews the existing KB-VQA methods. This survey aims to fill this gap by establishing a structured taxonomy of KB-VQA approaches, and categorizing the systems into main stages: knowledge representation, knowledge retrieval, and knowledge reasoning. By exploring various knowledge integration techniques and identifying persistent challenges, this work also outlines promising future research directions, providing a foundation for advancing KB-VQA models and their applications.

翻译：基于知识的视觉问答（KB-VQA）在通用视觉问答（VQA）基础上，不仅要求理解视觉与文本输入，还需运用广泛的知识，从而推动了各类实际应用的重要进展。KB-VQA面临独特挑战，包括对齐来自不同模态和来源的异构信息、从含噪或大规模知识库中检索相关知识，以及执行复杂推理以从融合语境中推断答案。随着大语言模型（LLMs）的进步，KB-VQA系统也经历了显著变革：LLMs作为强大的知识库、检索增强生成器及强推理器发挥作用。尽管已有显著进展，但目前尚无系统梳理现有KB-VQA方法的综合综述。本综述旨在填补这一空白，通过构建KB-VQA方法的结构化分类体系，将系统划分为知识表示、知识检索与知识推理三大核心阶段。通过探索多种知识集成技术并识别持续性挑战，本文还指出了未来富有前景的研究方向，为推进KB-VQA模型及其应用奠定基础。