3 papers
cs.LG2026
UniFed-VLM: Federated Instruction Tuning for Vision-Language Models with Multiple Heterogeneity
Pengyu Wang, Baochen Xiong, Xiaoshan Yang +4
Vision-Language Models (VLMs) have demonstrated strong performance in multimodal understanding and generation. However, fine-tuning of VLMs typically relies on centralized data, wh…
cs.CV2026
PivotMerge: Bridging Heterogeneous Multimodal Pre-training via Post-Alignment Model Merging
Zibo Shao, Baochen Xiong, Xiaoshan Yang +4
Multimodal Large Language Models (MLLMs) rely on multimodal pre-training over diverse data sources, where different datasets often induce complementary cross-modal alignment capabi…
cs.LG2023
A Recent Survey of Heterogeneous Transfer Learning
Runxue Bao, Yiming Sun, Yuhe Gao +4
The application of transfer learning, leveraging knowledge from source domains to enhance model performance in a target domain, has significantly grown, supporting diverse real-wor…