Knowledge-based Visual Question Answering (KB-VQA) extends general Visual Question Answering by requiring external knowledge beyond the provided visual and textual inputs, facilitating more complex 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 large-scale and noisy repositories, and the execution of complex reasoning to infer answers from the combined context. With the advancement of large language models, KB-VQA systems have undergone a notable transformation, where LLMs serve as powerful knowledge repositories, retrieval-augmented generators and strong reasoners. Despite substantial progress, there is a lack of a recent, systematic survey that organizes and reviews the evolving landscape of existing KB-VQA methods. This survey aims to fill this gap by establishing a structured taxonomy of KB-VQA approaches and decomposing mainstream systems into three fundamental stages: knowledge representation, knowledge retrieval, and knowledge reasoning. Through an examination of existing techniques employed at each stage, this survey identifies persistent challenges and outlines promising future research directions, providing a foundation for advancing KB-VQA models and their applications.

Product titles on e-commerce marketplaces often suffer from verbosity and inaccuracy, hindering effective communication of essential product details to customers. Refining titles to be more concise and informative is crucial for better user experience and product promotion. Recent solutions to product title refinement follow the standard text extractive and generative methods. Some also leverage multimodal information, e.g. using product images to supplement original titles with visual knowledge. However, these generative methods often produce additional terms not endorsed by sellers. Thus, it remains challenging to incorporate visual information missing from original titles into refined titles without excessively introducing novel terms. Additionally, most existing methods require human-labeled datasets, which are laborious to construct. In response to the two challenges, we present a self-supervised multimodal framework (HLATR) for title refinement that comprises two key modules: (1) a perturbated sample generator that constructs training data by systematically mining homogeneous listing information and (2) a title refinement network that effectively harnesses visual information to refine the original titles. To explicitly balance the extraction from original titles and the generation of supplementary novel terms, we adapt the copy mechanism that is guided by a focused refinement loss. Extensive experiments demonstrate that our proposed framework consistently outperforms others in generating refined titles that contain essential multimodal semantics with minimal deviation from the original ones.