Disen Hu — accepted AI-conference papers

2026

Hyper-Opinion Vagueness Quantification for Robust Multimodal Learning

Disen Hu, Xun Jiang, Xiaofeng Cao, Zheng Wang, Jingkuan Song, Heng Tao Shen +1

AAAI 2026technical

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 ``u

Cited by 0 Source ↗PDF ↗Scholar ↗

2026

Multimodal Learning on Low-Quality Data with Conformal Predictive Self-Calibration

Xun Jiang, Yufan Gu, Disen Hu, Yuqing Hou, Yazhou Yao, Fumin Shen +2

CVPR 2026

Multimodal learning often grapples with the challenge of low-quality data, which predominantly manifests as two facets: modality imbalance and noisy corruption. While these issues are often studied in isolation, we argue that they share a common root in the predictive uncertainty towards the reliabi

Cited by 0 Source ↗code ↗Scholar ↗