Visual Prompt Tuning

• URL: http://arxiv.org/abs/2203.12119v1
• Date: Wed, 23 Mar 2022 01:17:16 GMT
• Title: Visual Prompt Tuning
• Authors: Menglin Jia and Luming Tang and Bor-Chun Chen and Claire Cardie and Serge Belongie and Bharath Hariharan and Ser-Nam Lim
• Abstract summary: This paper introduces Visual Prompt Tuning (VPT) as an efficient and effective alternative to full fine-tuning for large-scale Transformer models in vision. VPT introduces only a small amount (less than 1% of model parameters) of trainable parameters in the input space while keeping the model backbone frozen.
• Score: 74.5309408185523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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) as an efficient and effective alternative to full fine-tuning for large-scale Transformer models in vision. Taking inspiration from recent advances in efficiently tuning large language models, VPT introduces only a small amount (less than 1% of model parameters) of trainable parameters in the input space while keeping the model backbone frozen. Via extensive experiments on a wide variety of downstream recognition tasks, we show that VPT achieves significant performance gains compared to other parameter efficient tuning protocols. Most importantly, VPT even outperforms full fine-tuning in many cases across model capacities and training data scales, while reducing per-task storage cost.

Related papers

• Visual Fourier Prompt Tuning [63.66866445034855]
  We propose the Visual Fourier Prompt Tuning (VFPT) method as a general and effective solution for adapting large-scale transformer-based models. Our approach incorporates the Fast Fourier Transform into prompt embeddings and harmoniously considers both spatial and frequency domain information. Our results demonstrate that our approach outperforms current state-of-the-art baselines on two benchmarks.
  arXiv  Detail & Related papers  (2024-11-02T18:18:35Z)
• CVPT: Cross-Attention help Visual Prompt Tuning adapt visual task [15.642102189777072]
  Cross Visual Prompt Tuning is a new type of visual fine-tuning. CVPT calculates cross-attention between the prompt tokens and the embedded tokens, which allows us to compute the semantic relationship between them. CVPT significantly improves VPT's performance and efficiency in visual tasks.
  arXiv  Detail & Related papers  (2024-08-27T11:07:19Z)
• E^2VPT: An Effective and Efficient Approach for Visual Prompt Tuning [55.50908600818483]
  Fine-tuning large-scale pretrained vision models for new tasks has become increasingly parameter-intensive. We propose an Effective and Efficient Visual Prompt Tuning (E2VPT) approach for large-scale transformer-based model adaptation. Our approach outperforms several state-of-the-art baselines on two benchmarks.
  arXiv  Detail & Related papers  (2023-07-25T19:03:21Z)
• Approximated Prompt Tuning for Vision-Language Pre-trained Models [54.326232586461614]
  In vision-language pre-trained models, prompt tuning often requires a large number of learnable tokens to bridge the gap between the pre-training and downstream tasks. We propose a novel Approximated Prompt Tuning (APT) approach towards efficient VL transfer learning.
  arXiv  Detail & Related papers  (2023-06-27T05:43:47Z)
• PVP: Pre-trained Visual Parameter-Efficient Tuning [29.05396521860764]
  Large-scale pre-trained transformers have demonstrated remarkable success in various computer vision tasks. It is still highly challenging to fully fine-tune these models for downstream tasks due to their high computational and storage costs. We propose a Pre-trained Visual. efficient (PVP) Tuning framework, which pre-trains the parameter-efficient tuning modules first and then leverages the pre-trained modules.
  arXiv  Detail & Related papers  (2023-04-26T15:55:29Z)
• Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models [64.49254199311137]
  We propose a novel Instance-aware Dynamic Prompt Tuning (IDPT) strategy for pre-trained point cloud models. The essence of IDPT is to develop a dynamic prompt generation module to perceive semantic prior features of each point cloud instance. In experiments, IDPT outperforms full fine-tuning in most tasks with a mere 7% of the trainable parameters.
  arXiv  Detail & Related papers  (2023-04-14T16:03:09Z)
• Towards Efficient Visual Adaption via Structural Re-parameterization [76.57083043547296]
  We propose a parameter-efficient and computational friendly adapter for giant vision models, called RepAdapter. RepAdapter outperforms full tuning by +7.2% on average and saves up to 25% training time, 20% GPU memory, and 94.6% storage cost of ViT-B/16 on VTAB-1k.
  arXiv  Detail & Related papers  (2023-02-16T06:14:15Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.