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
this https URL

这篇论文引入了一种可解释的基于图的 VQA 方法，并在 GQA 数据集上展示了竞争性的性能，通过在问答过程中生成一个子图作为解释来提供决策过程的洞察力。

用于可解释图像问题回答的内在子图生成

Intrinsic Subgraph Generation for Interpretable Graph based Visual  Question Answering

Due to the superior performance of Graph Neural Networks (GNNs) in various
domains, there is an increasing interest in the GNN explanation problem
"\emph{which fraction of the input graph is the most crucial to decide the
model's decision?}" Existing explanation methods focus on the supervised
settings, \eg, node classification and graph classification, while the
explanation for unsupervised graph-level representation learning is still
unexplored. The opaqueness of the graph representations may lead to unexpected
risks when deployed for high-stake decision-making scenarios. In this paper, we
advance the Information Bottleneck principle (IB) to tackle the proposed
explanation problem for unsupervised graph representations, which leads to a
novel principle, \textit{Unsupervised Subgraph Information Bottleneck} (USIB).
We also theoretically analyze the connection between graph representations and
explanatory subgraphs on the label space, which reveals that the expressiveness
and robustness of representations benefit the fidelity of explanatory
subgraphs. Experimental results on both synthetic and real-world datasets
demonstrate the superiority of our developed explainer and the validity of our
theoretical analysis.

本文提出了一种基于信息瓶颈原理的无监督子图信息瓶颈（USIB）的图解释方法，通过理论分析表明图信息建模表示的精确性与鲁棒性有助于解释性子图的可靠性。实验结果表明所提出的模型优于现有模型。