Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning
Chunqing Ruan Hongjian Wang
Parameter efficient transfer learning (PETL) is an emerging research spot
that aims to adapt large-scale pre-trained models to downstream tasks. Recent
advances have achieved great success in saving storage and computation costs.
However, these methods do not take into account instance-specific visual clues
for visual tasks. In this paper, we propose a Dynamic Visual Prompt Tuning
framework (DVPT), which can generate a dynamic instance-wise token for each
image. In this way, it can capture the unique visual feature of each image,
which can be more suitable for downstream visual tasks. We designed a Meta-Net
module that can generate learnable prompts based on each image, thereby
capturing dynamic instance-wise visual features. Extensive experiments on a
wide range of downstream recognition tasks show that DVPT achieves superior
performance than other PETL methods. More importantly, DVPT even outperforms
full fine-tuning on 17 out of 19 downstream tasks while maintaining high
parameter efficiency. Our code will be released soon.