Abstract
Knowledge-based visual question answering (QA) aims to answer a question which requires visually-grounded external knowledge beyond image content itself. Answering complex questions that require multi-hop reasoning under weak supervision is considered as a challenging problem since i) no supervision is given to the reasoning process and ii) high-order semantics of multi-hop knowledge facts need to be captured. In this paper, we introduce a concept of hypergraph to encode high-level semantics of a question and a knowledge base, and to learn high-order associations between them. The proposed model, Hypergraph Transformer, constructs a question hypergraph and a query-aware knowledge hypergraph, and infers an answer by encoding inter-associations between two hypergraphs and intra-associations in both hypergraph itself. Extensive experiments on two knowledge-based visual QA and two knowledge-based textual QA demonstrate the effectiveness of our method, especially for multi-hop reasoning problem. Our source code is available at https://github.com/yujungheo/kbvqa-public.- Anthology ID:
- 2022.acl-long.29
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 373–390
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.29
- DOI:
- 10.18653/v1/2022.acl-long.29
- Cite (ACL):
- Yu-Jung Heo, Eun-Sol Kim, Woo Suk Choi, and Byoung-Tak Zhang. 2022. Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 373–390, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Hypergraph Transformer: Weakly-Supervised Multi-hop Reasoning for Knowledge-based Visual Question Answering (Heo et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.acl-long.29.pdf
- Code
- yujungheo/kbvqa-public
- Data
- DBpedia, Visual Question Answering