Exploring Effective Unfolding Covering Prompt Tuning for Vision Mamba
Mingwang Wu, Yuetong Luo, Yankong Zhang, Bo Zhou, Shengeng Tang, Lechao Cheng · 2025
The Vision Mamba proposed recently has emerged as a popular architecture solution known for its efficiency in computational resources. However, the core designation of parallelized selective scan operation poses a challenge when performing visual prompt tuning on downstream tasks. The difficulty arises from the conflict between the unordered insertion of tokens in visual prompt tuning and the varying importance of tokens at different positions in vision mamba. To alleviate this issue, we propose an Unfolding Covering prompt tuning strategy that effectively customizes downstream tasks. In this work, we explore several visual prompting strategies to further improve performance with limited data. Exhaustive experiments on general tasks like classification and detection have demonstrated the superiority of our approach.