16 citations · 16 across the 5 of their papers we have counts for
5 papers
MoPD: Mixture-of-Prompts Distillation for Vision-Language Models
Yang Chen, Shuai Fu, Yu Zhang
Soft prompt learning methods are effective for adapting vision-language models (VLMs) to downstream tasks. Nevertheless, empirical evidence reveals a tendency of existing methods t…
Nemesis: Normalizing the Soft-prompt Vectors of Vision-Language Models
Shuai Fu, Xiequn Wang, Qiushi Huang +1
With the prevalence of large-scale pretrained vision-language models (VLMs), such as CLIP, soft-prompt tuning has become a popular method for adapting these models to various downs…
Enhancing Sharpness-Aware Minimization by Learning Perturbation Radius
Xuehao Wang, Weisen Jiang, Shuai Fu +1
Sharpness-aware minimization (SAM) is to improve model generalization by searching for flat minima in the loss landscape. The SAM update consists of one step for computing the pert…
Learning Retrieval Augmentation for Personalized Dialogue Generation
Qiushi Huang, Shuai Fu, Xubo Liu +4
Personalized dialogue generation, focusing on generating highly tailored responses by leveraging persona profiles and dialogue context, has gained significant attention in conversa…
GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning
Yanbin Wei, Shuai Fu, Weisen Jiang +5
Large Language Models (LLMs) are increasingly used for various tasks with graph structures. Though LLMs can process graph information in a textual format, they overlook the rich vi…