375 citations · 2.2k across the 69 of their papers we have counts for
4 papers · 1 filter
FeDaL: Federated Dataset Learning for General Time Series Foundation Models
Shengchao Chen, Guodong Long, Michael Blumenstein +1
Dataset-level heterogeneity introduces significant domain biases that fundamentally degrade generalization on general Time Series Foundation Models (TSFMs), yet this challenge rema…
Raw Data Matters: Enhancing Prompt Tuning by Internal Augmentation on Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +4
For CLIP-based prompt tuning, introducing more data as additional knowledge for enhancing fine-tuning process is proved to be an effective approach. Existing data amplification str…
FedMerge: Federated Personalization via Model Merging
Shutong Chen, Tianyi Zhou, Guodong Long +2
One global model in federated learning (FL) might not be sufficient to serve many clients with non-IID tasks and distributions. While there has been advances in FL to train multipl…
DPC: Dual-Prompt Collaboration for Tuning Vision-Language Models
Haoyang Li, Liang Wang, Chao Wang +3
The Base-New Trade-off (BNT) problem universally exists during the optimization of CLIP-based prompt tuning, where continuous fine-tuning on base (target) classes leads to a simult…