Abstract
Knowledge-based referring expression comprehension (KB-REC) aims to identify visual objects referred to by expressions that incorporate knowledge. Existing methods employ sentence-level retrieval and fusion methods, which may lead to issues of similarity bias and interference from irrelevant information in unstructured knowledge sentences. To address these limitations, we propose a segment-level and category-oriented network (SLCO). Our approach includes a segment-level and prompt-based knowledge retrieval method to mitigate the similarity bias problem and a category-based grounding method to alleviate interference from irrelevant information in knowledge sentences. Experimental results show that our SLCO can eliminate interference and improve the overall performance of the KB-REC task.- Anthology ID:
- 2023.findings-acl.557
- Volume:
- Findings of the Association for Computational Linguistics: ACL 2023
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8745–8757
- Language:
- URL:
- https://aclanthology.org/2023.findings-acl.557
- DOI:
- 10.18653/v1/2023.findings-acl.557
- Cite (ACL):
- Yuqi Bu, Xin Wu, Liuwu Li, Yi Cai, Qiong Liu, and Qingbao Huang. 2023. Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension. In Findings of the Association for Computational Linguistics: ACL 2023, pages 8745–8757, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension (Bu et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/2023.findings-acl.557.pdf