  Fine-tuning visual models has been widely shown promising performance on many
downstream visual tasks. With the surprising development of pre-trained visual
foundation models, visual tuning jumped out of the standard modus operandi that
fine-tunes the whole pre-trained model or just the fully connected layer.
Instead, recent advances can achieve superior performance than full-tuning the
whole pre-trained parameters by updating far fewer parameters, enabling edge
devices and downstream applications to reuse the increasingly large foundation
models deployed on the cloud. With the aim of helping researchers get the full
picture and future directions of visual tuning, this survey characterizes a
large and thoughtful selection of recent works, providing a systematic and
comprehensive overview of existing work and models. Specifically, it provides a
detailed background of visual tuning and categorizes recent visual tuning
techniques into five groups: prompt tuning, adapter tuning, parameter tuning,
and remapping tuning. Meanwhile, it offers some exciting research directions
for prospective pre-training and various interactions in visual tuning.
