Maoyuan Shao — accepted AI-conference papers

2026

CAPT: Confusion-Aware Prompt Tuning for Reducing Vision-Language Misalignment

Maoyuan Shao, Yutong Gao, Xinyang Huang, Lijuan Sun, Guoshun Nan, Chuang Zhu

CVPR 2026

Vision-language models like CLIP have achieved remarkable progress in cross-modal representation learning, yet suffer from systematic misclassifications among visually and semantically similar categories. We observe that such confusion patterns are not random but persistently occur between specific

Cited by 0 Source ↗code ↗Scholar ↗

2025

Spotlighter: Revisiting Prompt Tuning from a Representative Mining View

Yutong Gao, Maoyuan Shao, Xinyang Huang, Chuang Zhu, Yu Weng, Xuan Liu +2

EMNLP 2025

CLIP’s success has demonstrated that prompt tuning can achieve robust cross-modal semantic alignment for tasks ranging from open-domain recognition to fine-grained classification. However, redundant or weakly relevant feature components introduce noise and incur unnecessary computational costs. In t

Cited by 0 Source ↗PDF ↗Scholar ↗