@inproceedings{fc7a72df1087404682f778692e51215f,
title = "Syntax Tree Constrained Graph Network for Visual Question Answering",
abstract = "Visual Question Answering (VQA) aims to automatically answer natural language questions related to given image content. Existing VQA methods integrate vision modeling and language understanding to explore the deep semantics of the question. However, these methods ignore the significant syntax information of the question, which plays a vital role in understanding the essential semantics of the question and guiding the visual feature refinement. To fill the gap, we suggested a novel Syntax Tree Constrained Graph Network (STCGN) for VQA based on entity message passing and syntax tree. This model is able to extract a syntax tree from questions and obtain more precise syntax information. Specifically, we parse questions and obtain the question syntax tree using the Stanford syntax parsing tool. From the word level and phrase level, syntactic phrase features and question features are extracted using a hierarchical tree convolutional network. We then design a message-passing mechanism for phrase-aware visual entities and capture entity features according to a given visual context. Extensive experiments on VQA2.0 datasets demonstrate the superiority of our proposed model.",
keywords = "Graph neural network, Message passing, Syntax tree, Tree convolution, Visual question answering",
author = "Xiangrui Su and Qi Zhang and Chongyang Shi and Jiachang Liu and Liang Hu",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 30th International Conference on Neural Information Processing, ICONIP 2023 ; Conference date: 20-11-2023 Through 23-11-2023",
year = "2024",
doi = "10.1007/978-981-99-8073-4_10",
language = "English",
isbn = "9789819980727",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "122--136",
editor = "Biao Luo and Long Cheng and Zheng-Guang Wu and Hongyi Li and Chaojie Li",
booktitle = "Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings",
address = "Germany",
}