BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining
DOI:
https://doi.org/10.1609/aaai.v38i16.29798Keywords:
NLP: Language Grounding & Multi-modal NLP, NLP: Machine Translation, Multilinguality, Cross-Lingual NLPAbstract
The current research direction in generative models, such as the recently developed GPT4, aims to find relevant knowledge information for multimodal and multilingual inputs to provide answers. Under these research circumstances, the demand for multilingual evaluation of visual question answering (VQA) tasks, a representative task of multimodal systems, has increased. Accordingly, we propose a bilingual outside-knowledge VQA (BOK-VQA) dataset in this study that can be extended to multilingualism. The proposed data include 17K images, 17K question-answer pairs for both Korean and English and 280K instances of knowledge information related to question-answer content. We also present a framework that can effectively inject knowledge information into a VQA system by pretraining the knowledge information of BOK-VQA data in the form of graph embeddings. Finally, through in-depth analysis, we demonstrated the actual effect of the knowledge information contained in the constructed training data on VQA.Downloads
Published
2024-03-24
How to Cite
Kim, M., Song, S., Lee, Y., Jang, H., & Lim, K. (2024). BOK-VQA: Bilingual outside Knowledge-Based Visual Question Answering via Graph Representation Pretraining. Proceedings of the AAAI Conference on Artificial Intelligence, 38(16), 18381-18389. https://doi.org/10.1609/aaai.v38i16.29798
Issue
Section
AAAI Technical Track on Natural Language Processing I