2 citations · 3 across the 4 of their papers we have counts for
4 papers
NLPrompt: Noise-Label Prompt Learning for Vision-Language Models
Bikang Pan, Qun Li, Xiaoying Tang +6
The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite…
Federated Learning from Vision-Language Foundation Models: Theoretical Analysis and Method
Bikang Pan, Wei Huang, Ye Shi
Integrating pretrained vision-language foundation models like CLIP into federated learning has attracted significant attention for enhancing generalization across diverse tasks. Ty…
Global and Local Prompts Cooperation via Optimal Transport for Federated Learning
Hongxia Li, Wei Huang, Jingya Wang +1
Prompt learning in pretrained visual-language models has shown remarkable flexibility across various downstream tasks. Leveraging its inherent lightweight nature, recent research a…
Fed-CO2: Cooperation of Online and Offline Models for Severe Data Heterogeneity in Federated Learning
Zhongyi Cai, Ye Shi, Wei Huang +1
Federated Learning (FL) has emerged as a promising distributed learning paradigm that enables multiple clients to learn a global model collaboratively without sharing their private…