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
VideoMiner: Iteratively Grounding Key Frames of Hour-Long Videos via Tree-based Group Relative Policy Optimization
Xinye Cao, Hongcan Guo, Jiawen Qian +6
Understanding hour-long videos with multi-modal large language models (MM-LLMs) enriches the landscape of human-centered AI applications. However, for end-to-end video understandin…
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.…