Intrinsic Subgraph Generation for Interpretable Graph based Visual Question Answering
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.
Code (1)
Tasks
Decision MakingExplainable artificial intelligenceExplainable Artificial Intelligence (XAI)Question AnsweringVisual Question AnsweringVisual Question Answering (VQA)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Discrete Subgraph Sampling for Interpretable Graph based Visual Question Answering
Explainable artificial intelligence (XAI) aims to make machine learning models more transparent. While many approaches focus on generating explanations post-hoc, interpretable approaches, which generate the explanations …
Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)Question AnsweringVisual Question AnsweringTopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
Graph Neural Networks (GNNs) have shown remarkable success across various scientific fields, yet their adoption in critical decision-making is often hindered by a lack of interpretability. Recently, intrinsically interpr…
Graph LearningHow Interpretable Are Interpretable Graph Neural Networks?
Interpretable graph neural networks (XGNNs ) are widely adopted in various scientific applications involving graph-structured data. Existing XGNNs predominantly adopt the attention-based mechanism to learn edge or node i…
Graph ClassificationUnveiling Global Interactive Patterns across Graphs: Towards Interpretable Graph Neural Networks
Graph Neural Networks (GNNs) have emerged as a prominent framework for graph mining, leading to significant advances across various domains. Stemmed from the node-wise representations of GNNs, existing explanation studie…
ClusteringGraph ClassificationGraph ClusteringGraph MiningGenerative Subgraph Contrast for Self-Supervised Graph Representation Learning
Contrastive learning has shown great promise in the field of graph representation learning. By manually constructing positive/negative samples, most graph contrastive learning methods rely on the vector inner product bas…
Contrastive LearningGraph Representation LearningNode ClassificationRepresentation Learning