Papers › GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering

GraghVQA: Language-Guided Graph Neural Networks for Graph-based Visual Question Answering

20 Apr 2021NAACL (maiworkshop) 2021 6arXiv:2104.10283archive 2025-07-28

Weixin Liang, Yanhao Jiang, Zixuan Liu

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%).

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Code

codexxxl/GraphVQA officialmentioned in papermentioned on GitHubpytorch report

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Tasks

Graph Neural NetworkGraph Question AnsweringQuestion AnsweringVisual Question AnsweringVisual Question Answering (VQA)

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Graph Question Answering GQA GraphVQA Accuracy 96.30 #1 of 2 Archive leaderboard report

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Methods

Graph Neural Network

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