3 papers
cs.CV2026
Adapting Vision Foundation Models with Cascaded Semantics
Xi Xiao, Xingjian Li, Cheng Han +8
Prompt tuning, a leading parameter-efficient adaptation paradigm in NLP, has recently been extended to computer vision. Visual prompt tuning (VPT) adapts pre-trained vision transfo…
cs.CV2026
Not All Directions Matter: Towards Structured and Task-Aware Low-Rank Model Adaptation
Xi Xiao, Chenrui Ma, Yunbei Zhang +7
Low-Rank Adaptation (LoRA) has become a cornerstone of parameter-efficient fine-tuning (PEFT). Yet, its efficacy is hampered by two fundamental limitations: semantic drift, by trea…
cs.CV2024
Object Pose Estimation via the Aggregation of Diffusion Features
Tianfu Wang, Guosheng Hu, Hongguang Wang
Estimating the pose of objects from images is a crucial task of 3D scene understanding, and recent approaches have shown promising results on very large benchmarks. However, these…