Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering

• URL: http://arxiv.org/abs/2403.17647v2
• Date: Wed, 27 Mar 2024 10:07:59 GMT
• Title: Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering
• Authors: Pascal Tilli, Ngoc Thang Vu,
• Abstract summary: We introduce an interpretable approach for graph-based Visual Question Answering (VQA) Our model is designed to intrinsically produce a subgraph during the question-answering process as its explanation. We compare these generated subgraphs against established post-hoc explainability methods for graph neural networks, and perform a human evaluation.
• Score: 27.193336817953142
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The large success of deep learning based methods in Visual Question Answering (VQA) has concurrently increased the demand for explainable methods. Most methods in Explainable Artificial Intelligence (XAI) focus on generating post-hoc explanations rather than taking an intrinsic approach, the latter characterizing an interpretable model. In this work, we introduce an interpretable approach for graph-based VQA and demonstrate competitive performance on the GQA dataset. This approach bridges the gap between interpretability and performance. Our model is designed to intrinsically produce a subgraph during the question-answering process as its explanation, providing insight into the decision making. To evaluate the quality of these generated subgraphs, we compare them against established post-hoc explainability methods for graph neural networks, and perform a human evaluation. Moreover, we present quantitative metrics that correlate with the evaluations of human assessors, acting as automatic metrics for the generated explanatory subgraphs. Our implementation is available at https://github.com/DigitalPhonetics/Intrinsic-Subgraph-Generation-for-VQA.

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