@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://preview.aclanthology.org/ingest_wac_2008/2023.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."
}