Visual explainable artificial intelligence for graph-based visual question answering and scene graph curation

Visual explainable artificial intelligence for graph-based visual question answering and scene graph curation

Abstract This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering (VQA) systems. The method focuses on identifying false answer predictions by the model and offers users the opportunity to directly correct mistakes in the inp...

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Bibliographic Details
Main Authors: Sebastian Künzel, Tanja Munz-Körner, Pascal Tilli, Noel Schäfer, Sandeep Vidyapu, Ngoc Thang Vu, Daniel Weiskopf
Format: Article
Language:English
Published: SpringerOpen 2025-04-01
Series:Visual Computing for Industry, Biomedicine, and Art
Subjects:
Visual question answering
Explainable artificial intelligence
Visual analytics
Scene graphs
Online Access:https://doi.org/10.1186/s42492-025-00185-y
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https://doi.org/10.1186/s42492-025-00185-y

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