@inproceedings{niu-etal-2026-bridging,
    title = "Bridging the Sensory Gap: Visual Injection for Taxonomy Completion",
    author = "Niu, Yuhang  and
      Xu, Hongyuan  and
      Liu, Ciyi  and
      Wei, Bofan  and
      Ye, Jiaqi  and
      Wen, Yanlong  and
      Yuan, Xiaojie",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.275/",
    pages = "6092--6107",
    ISBN = "979-8-89176-390-6",
    abstract = "Taxonomy Completion aims to automatically integrate new concepts into existing hierarchies. However, existing text-only methods suffer from a ``Sensory Gap'': they struggle to differentiate ambiguous definitions (e.g., Latte vs. Cappuccino) and miss visual grouping signals. Consequently, they often misinterpret lexical overlaps as hierarchical dependencies, leading to erroneous structural predictions. To bridge this, we propose VITC, a framework leveraging Visual Injection for Taxonomy Completion. By mapping synthesized images into intrinsic pseudo-tokens, we enable the text encoder to perform holistic structural reasoning. To address injection challenges, we introduce Adaptive Residual Fusion, which decouples magnitude from selection to prevent visual signals from being drowned out, and the Multimodal Guided Adaptive Reweighting strategy, which leverages cross-modal consensus (Mutual Rescue and Complementary Mining) to filter noise and identify hard negatives. Experiments on three datasets demonstrate that VITC achieves state-of-the-art performance, delivering an average absolute gain of over 19{\%} in Hit@1. Code is available at https://github.com/nyh-a/VITC."
}