2 citations · 6 across the 4 of their papers we have counts for
3 papers · 1 filter
Semantic Prompt Learning for Weakly-Supervised Semantic Segmentation
Ci-Siang Lin, Chien-Yi Wang, Yu-Chiang Frank Wang +1
Weakly-Supervised Semantic Segmentation (WSSS) aims to train segmentation models using image data with only image-level supervision. Since precise pixel-level annotations are not a…
Language-Guided Transformer for Federated Multi-Label Classification
I-Jieh Liu, Ci-Siang Lin, Fu-En Yang +1
Federated Learning (FL) is an emerging paradigm that enables multiple users to collaboratively train a robust model in a privacy-preserving manner without sharing their private dat…
Efficient Model Personalization in Federated Learning via Client-Specific Prompt Generation
Fu-En Yang, Chien-Yi Wang, Yu-Chiang Frank Wang
Federated learning (FL) emerges as a decentralized learning framework which trains models from multiple distributed clients without sharing their data to preserve privacy. Recently…