ROBUSTNESS

We investigate the robustness of four mainstream adaptation methods: full fine-tuning, soft prompt, LoRA, and adapter-based methods including Adapter , Hyperformer, and Compacter. To better understand the robustness of these adaptation methods, we also consider the information sharing across tasks. Therefore, for soft prompt, LoRA, and Compacters, we conduct experiments in both single and multiple manners. The single manner uses one adaptation model for all tasks, while the multiple manner uses independent adaptation modules for different tasks. For Adapter, besides the single and multiple manners, we also adopt the half-shared manner, where only the undersampling module in adapters is shared across tasks. In total, we have eleven adaptation methods... Various adaptation methods, such as LoRA, prompts, and adapters, have been proposed to enhance the performance of pre-trained vision-language models in specific domains. As test samples in real-world applications usually differ from..