Hyper-Opinion Vagueness Quantification for Robust Multimodal Learning

Disen Hu; Xun Jiang; Xiaofeng Cao; Zheng Wang; Jingkuan Song; Heng Tao Shen; Xing Xu

AAAI 2026technical0 citations

Hyper-Opinion Vagueness Quantification for Robust Multimodal Learning

Disen Hu, Xun Jiang, Xiaofeng Cao, Zheng Wang, Jingkuan Song, Heng Tao Shen, Xing Xu

Abstract

Robust Multimodal Learning (RML) aims to address the issues of unreliable predictions of multimodal models. Nevertheless, previous RML works often struggle to distinguish between different categories that rely on identical intra-modal cues, making ambiguous predictions. We defined this degree of ``uncertain

BibTeX

@inproceedings{aaai2026_hyperopinionvagu,
  title = {Hyper-Opinion Vagueness Quantification for Robust Multimodal Learning},
  author = {Disen Hu and Xun Jiang and Xiaofeng Cao and Zheng Wang and Jingkuan Song and Heng Tao Shen and Xing Xu},
  booktitle = {AAAI 2026},
  year = {2026}
}