# GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering Source: [GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-12-14T17:55:05.205Z Authors: Weixin Liang, Yanhao Jiang, Zixuan Liu Published: 2021-04-20T23:54:41.000Z ## Abstract Images are more than a collection of objects or attributes -- they represent a web of relationships among interconnected objects. Scene Graph has emerged as a new modality for a structured graphical representation of images. Scene Graph encodes objects as nodes connected via pairwise relations as edges. To support question answering on scene graphs, we propose GraphVQA, a language-guided graph neural network framework that translates and executes a natural language question as multiple iterations of message passing among graph nodes. We explore the design space of GraphVQA framework, and discuss the trade-off of different design choices. Our experiments on GQA dataset show that GraphVQA outperforms the state-of-the-art model by a large margin (88.43% vs. 94.78%). ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()