Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension

Yuqi Bu, Xin Wu, Liuwu Li, Yi Cai, Qiong Liu, Qingbao Huang


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
Bibkey:
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)
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PDF:
https://aclanthology.org/2023.findings-acl.557.pdf