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cs.LG2025
GateRA: Token-Aware Modulation for Parameter-Efficient Fine-Tuning
Jie Ou, Shuaihong Jiang, Yingjun Du +1
Parameter-efficient fine-tuning (PEFT) methods, such as LoRA, DoRA, and HiRA, enable lightweight adaptation of large pre-trained models via low-rank updates. However, existing PEFT…
cs.LG2024
Prompt Diffusion Robustifies Any-Modality Prompt Learning
Yingjun Du, Gaowen Liu, Yuzhang Shang +3
Foundation models enable prompt-based classifiers for zero-shot and few-shot learning. Nonetheless, the conventional method of employing fixed prompts suffers from distributional s…
cs.LG2024
IPO: Interpretable Prompt Optimization for Vision-Language Models
Yingjun Du, Wenfang Sun, Cees G. M. Snoek
Pre-trained vision-language models like CLIP have remarkably adapted to various downstream tasks. Nonetheless, their performance heavily depends on the specificity of the input tex…