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.
BibTeX
@inproceedings{bu-etal-2023-segment,
title = "Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension",
author = "Bu, Yuqi and
Wu, Xin and
Li, Liuwu and
Cai, Yi and
Liu, Qiong and
Huang, Qingbao",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.557/",
doi = "10.18653/v1/2023.findings-acl.557",
pages = "8745--8757"
}