1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.CV2025
DA-VPT: Semantic-Guided Visual Prompt Tuning for Vision Transformers
Li Ren, Chen Chen, Liqiang Wang +1
Visual Prompt Tuning (VPT) has become a promising solution for Parameter-Efficient Fine-Tuning (PEFT) approach for Vision Transformer (ViT) models by partially fine-tuning learnabl…
cs.CV2024★ 1 cited
Learning Semantic Proxies from Visual Prompts for Parameter-Efficient Fine-Tuning in Deep Metric Learning
Li Ren, Chen Chen, Liqiang Wang +1
Deep Metric Learning (DML) has long attracted the attention of the machine learning community as a key objective. Existing solutions concentrate on fine-tuning the pre-trained mode…
cs.CV2024
Towards Improved Proxy-based Deep Metric Learning via Data-Augmented Domain Adaptation
Li Ren, Chen Chen, Liqiang Wang +1
Deep Metric Learning (DML) plays an important role in modern computer vision research, where we learn a distance metric for a set of image representations. Recent DML techniques ut…