As the size of transformer-based models continues to grow, fine-tuning these large-scale
pretrained vision models for new tasks has become increasingly parameter-intensive …

The current modus operandi in adapting pre-trained models involves updating all the
backbone parameters, ie., full fine-tuning. This paper introduces Visual Prompt Tuning (VPT) …

This paper presents a simple and effective visual prompting method for adapting pre-trained
models to downstream recognition tasks. Our method includes two key designs. First, rather …

With the introduction of the transformer architecture in computer vision, increasing model
scale has been demonstrated as a clear path to achieving performance and robustness …

In computer vision, fine-tuning is the de-facto approach to leverage pre-trained vision
models to perform downstream tasks. However, deploying it in practice is quite challenging …

Despite recent promising performances across a range of vision tasks, vision Transformers
still have an issue of high computational costs. Recently, vision prompt learning has …

Prompt learning is an efficient approach to adapt transformers by inserting learnable set of
parameters into the input and intermediate representations of a pre-trained model. In this …

We investigate the efficacy of visual prompting to adapt large-scale models in vision.
Following the recent approach from prompt tuning and adversarial reprogramming, we learn …

Intermediate features of a pre-trained model have been shown informative for making
accurate predictions on downstream tasks, even if the model backbone is frozen. The key …

Large-scale pre-trained vision models (PVMs) have shown great potential for adaptability
across various downstream vision tasks. However, with state-of-the-art PVMs growing to …