@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",
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.",
}
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<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.</abstract>
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%0 Conference Proceedings
%T Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension
%A Bu, Yuqi
%A Wu, Xin
%A Li, Liuwu
%A Cai, Yi
%A Liu, Qiong
%A Huang, Qingbao
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F bu-etal-2023-segment
%X 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.
%R 10.18653/v1/2023.findings-acl.557
%U https://aclanthology.org/2023.findings-acl.557
%U https://doi.org/10.18653/v1/2023.findings-acl.557
%P 8745-8757
Markdown (Informal)
[Segment-Level and Category-Oriented Network for Knowledge-Based Referring Expression Comprehension](https://aclanthology.org/2023.findings-acl.557) (Bu et al., Findings 2023)
ACL