4 papers
CFPO: Counterfactual Policy Optimization for Multimodal Reasoning
Zhangyuan Yu, Wanran Sun, Guangjing Yang +2
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities in multimodal reasoning. However, prevailing reinforcement learning (RL) paradigms lack explicit coun…
Stabilized Fine-Tuning with LoRA in Federated Learning: Mitigating the Side Effect of Client Size and Rank via the Scaling Factor
Jiayu Huang, Xiaohu Wu, Tiantian He +1
Large Language Models (LLMs) are pivotal in natural language processing. The impracticality of full fine-tuning has prompted Parameter-Efficient Fine-Tuning (PEFT) methods like Low…
iDPA: Instance Decoupled Prompt Attention for Incremental Medical Object Detection
Huahui Yi, Wei Xu, Ziyuan Qin +4
Existing prompt-based approaches have demonstrated impressive performance in continual learning, leveraging pre-trained large-scale models for classification tasks; however, the ti…
MLAE: Masked LoRA Experts for Visual Parameter-Efficient Fine-Tuning
Junjie Wang, Guangjing Yang, Wentao Chen +4
In response to the challenges posed by the extensive parameter updates required for full fine-tuning of large-scale pre-trained models, parameter-efficient fine-tuning (PEFT) metho…