  Pre-training & fine-tuning can enhance the transferring efficiency and
performance in visual tasks. Recent delta-tuning methods provide more options
for visual classification tasks. Despite their success, existing visual
delta-tuning art fails to exceed the upper limit of full fine-tuning on
challenging tasks like instance segmentation and semantic segmentation. To find
a competitive alternative to full fine-tuning, we propose the Multi-cognitive
Visual Adapter (Mona) tuning, a novel adapter-based tuning method. First, we
introduce multiple vision-friendly filters into the adapter to enhance its
ability to process visual signals, while previous methods mainly rely on
language-friendly linear filters. Second, we add the scaled normalization layer
in the adapter to regulate the distribution of input features for visual
filters. To fully demonstrate the practicality and generality of Mona, we
conduct experiments on multiple representative visual tasks, including instance
segmentation on COCO, semantic segmentation on ADE20K, object detection on
Pascal VOC, and image classification on several common datasets. Exciting
results illustrate that Mona surpasses full fine-tuning on all these tasks and
is the only delta-tuning method outperforming full fine-tuning on instance
segmentation and semantic segmentation tasks. For example, Mona achieves a 1%
performance gain on the COCO dataset compared to full fine-tuning.
Comprehensive results suggest that Mona-tuning is more suitable for retaining
and utilizing the capabilities of pre-trained models than full fine-tuning. The
code will be released at https://github.com/Leiyi-Hu/mona.
