1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2026
CAPT: Confusion-Aware Prompt Tuning for Reducing Vision-Language Misalignment
Maoyuan Shao, Yutong Gao, Xinyang Huang +3
Vision-language models like CLIP have achieved remarkable progress in cross-modal representation learning, yet suffer from systematic misclassifications among visually and semantic…
cs.CV2025
Spotlighter: Revisiting Prompt Tuning from a Representative Mining View
Yutong Gao, Maoyuan Shao, Xinyang Huang +5
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.…
cs.LG2023★ 1 cited
Refining Latent Homophilic Structures over Heterophilic Graphs for Robust Graph Convolution Networks
Chenyang Qiu, Guoshun Nan, Tianyu Xiong +6
Graph convolution networks (GCNs) are extensively utilized in various graph tasks to mine knowledge from spatial data. Our study marks the pioneering attempt to quantitatively inve…