Visual Tuning

arXiv:2305.06061v2 Announce Type: replace-cross
Abstract: 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 …

abstract advances arxiv cs.ai cs.cv cs.lg development fine-tuning foundation layer performance pre-trained model standard tasks type visual