5%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks

Pre-training & fine-tuning can enhance the transferring efficiency and performance in visual tasks. Recent deltatuning methods provide more options for visual classification tasks. Despite their success, existing visual deltatuning art fails to exceed the upper limit of full finetuning on challenging tasks. 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 for processing visual signals, while previous methods mainly rely on languagefriendly linear filters. Second, we add the scaled layernorm 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 representative visual tasks, including instance segmentation on COCO, semantic segmentation on ADE20K, object detection on Pascal VOC, oriented object detection on DOTA/STAR, and image classification on three common datasets. Exciting results illustrate that Mona surpasses full fine-tuning on all these tasks by tuning less than 5% params of the backbone, and is the only delta-tuning method outperforming full fine-tuning on all tasks. For example, Mona achieves 1% performance gain on the COCO compared to full fine-tuning. Comprehensive results suggest that Monatuning is more suitable for retaining and utilizing the capabilities of pre-trained models than full fine-tuning. The code is publicly available on https://github.com/Leiyi-Hu/mona.