Yuqing Hou — accepted AI-conference papers

2026

Codebook-Centric Deep Hashing: End-to-End Joint Learning of Semantic Hash Centers and Neural Hash Function

Shuo Yin, Zhiyuan Yin, Yuqing Hou, Rui Liu, Yong Chen, Dell Zhang

AAAI 2026technical

Hash center-based deep hashing methods improve upon pairwise or triplet-based approaches by assigning fixed hash centers to each class as learning targets, thereby avoiding the inefficiency of local similarity optimization. However, random center initialization often disregards inter-class semantic

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 ↗