Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning

• URL: http://arxiv.org/abs/2309.06123v1
• Date: Tue, 12 Sep 2023 10:47:37 GMT
• Title: Dynamic Visual Prompt Tuning for Parameter Efficient Transfer Learning
• Authors: Chunqing Ruan, Hongjian Wang
• Abstract summary: 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. Experiments on a wide range of downstream recognition tasks show that DVPT achieves superior performance than other PETL methods.
• Score: 0.8430481660019451
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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