4 papers
FedSmoothLoRA: Toward Smoother and Faster Convergence in Federated Low-Rank Adaptation
Zehao Wang, Guanglei Yang, Yihan Zeng +4
Federated fine-tuning of foundation models with Low-Rank Adaptation (LoRA) provides an efficient solution for reducing communication and computation costs while preserving data loc…
CGL: Advancing Continual GUI Learning via Reinforcement Fine-Tuning
Zhenquan Yao, Zitong Huang, Yihan Zeng +5
Graphical User Interface (GUI) Agents, benefiting from recent advances in multimodal large language models (MLLM), have achieved significant development. However, due to the freque…
Text to Image for Multi-Label Image Recognition with Joint Prompt-Adapter Learning
Chun-Mei Feng, Kai Yu, Xinxing Xu +4
Benefited from image-text contrastive learning, pre-trained vision-language models, e.g., CLIP, allow to direct leverage texts as images (TaI) for parameter-efficient fine-tuning (…
Unprejudiced Training Auxiliary Tasks Makes Primary Better: A Multi-Task Learning Perspective
Yuanze Li, Chun-Mei Feng, Qilong Wang +2
Human beings can leverage knowledge from relative tasks to improve learning on a primary task. Similarly, multi-task learning methods suggest using auxiliary tasks to enhance a neu…