# Learning Conditioned Graph Structures for Interpretable Visual Question Answering Source: [Learning Conditioned Graph Structures for Interpretable Visual Question Answering]() Collection index: [llms.txt]() Data updated: 2025-10-25T21:16:52.800Z Authors: Will Norcliffe-Brown, Efstathios Vafeias, Sarah Parisot Published: 2018-06-19T13:59:05.000Z ## Abstract Visual Question answering is a challenging problem requiring a combination of concepts from Computer Vision and Natural Language Processing. Most existing approaches use a two streams strategy, computing image and question features that are consequently merged using a variety of techniques. Nonetheless, very few rely on higher level image representations, which can capture semantic and spatial relationships. In this paper, we propose a novel graph-based approach for Visual Question Answering. Our method combines a graph learner module, which learns a question specific graph representation of the input image, with the recent concept of graph convolutions, aiming to learn image representations that capture question specific interactions. We test our approach on the VQA v2 dataset using a simple baseline architecture enhanced by the proposed graph learner module. We obtain promising results with 66.18% accuracy and demonstrate the interpretability of the proposed method. Code can be found at github.com/aimbrain/vqa-project. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Visual Question Answering on VQA v2 (test-std)](): Visual Question Answering; VQA v2 (test-std); Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()