A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task

• URL: http://arxiv.org/abs/2504.17547v1
• Date: Thu, 24 Apr 2025 13:37:25 GMT
• Title: A Comprehensive Survey of Knowledge-Based Vision Question Answering Systems: The Lifecycle of Knowledge in Visual Reasoning Task
• Authors: Jiaqi Deng, Zonghan Wu, Huan Huo, Guandong Xu,
• Abstract summary: Knowledge-based Vision Question Answering (KB-VQA) extends general Vision Question Answering (VQA)<n>No comprehensive survey currently exists that systematically organizes and reviews the existing KB-VQA methods.
• Score: 15.932332484902103
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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