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A collection of parameter-efficient transfer learning papers focusing on computer vision and multimodal domains.
| Date | Stars |
|---|---|
| 2026-07-24 | 412 |
| 2026-07-25 | 412 |
| 2026-07-28 | 412 |
| 2026-07-30 | 412 |
| 2026-07-31 | 412 |
| 2026-08-06 | 412 |
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# Awesome-Parameter-Efficient-Transfer-Learning A collection of parameter-efficient transfer learning papers focusing on computer vision and multimodal domains. ## Content - [Why Parameter Efficient?](#why-parameter-efficient) - [Keywords Convention](#keywords-convention) - [Papers](#papers) - [Prompt](#prompt) - [Adapter](#adapter) - [Unified](#unified) - [Others](#others) - [Contribution](#contribution) - [Contributors](#contributors) - [Contributing to this paper list](#contributing-to-this-paper-list) - [Acknowledgement](#acknowledgement) ## Why Parameter Efficient? Pre-training, then fully fine-tuning is a long standing paradigm in deep learning. However, as pre-trained models are scaling up, *e.g.* GPT-3(175B params), fully fine-tuning them on various downstream tasks has a high risk of overfitting. Moreover, in practice, it would be costly to train and store a large model for each task. To overcome the above issues, researchers started to explore **Parameter-Efficient Transfer Learning** which aims at adapting large-scale pre-trained model to various downstream tasks by modifying as less parameter as possible. Inspired by the great advances in NLP domain and the continuous trend of scaling up models, scholars in computer vision and multimodal domains also join the research craze. ## Keywords Convention We follow the general idea of [PromptPapers](https://github.com/thunlp/PromptPapers) to label the papers.  The abbreviation of the work.  The main explored task of the work.  Other important information of the work. ## Papers ### Prompt - **Learning to Prompt for Vision-Language Models,** IJCV 2022 (arXiv:2109.01134).    *Kaiyang Zhou, Jingkang Yang, Chen Change Loy, Ziwei Liu.* [[Paper](https://arxiv.org/abs/2109.01134)][[Code](https://github.com/KaiyangZhou/CoOp)] - **Prompting Visual-Language Models for Efficient Video Understanding,** ECCV 2022 (arXiv:2112.04478).   *Chen Ju, Tengda Han, Kunhao Zheng, Ya Zhang, Weidi Xie.* [[Paper](https://arxiv.org/abs/2112.04478)][[Code](https://github.com/ju-chen/Efficient-Prompt)] - **Domain Adaptation via Prompt Learning,** arXiv: arXiv:2202.06687.    *Chunjiang Ge, Rui Huang, Mixue Xie, Zihang Lai, Shiji Song, Shuang Li, Gao Huang.* [[Paper](https://arxiv.org/abs/2202.06687)][Code] - **Conditional Prompt Learning for Vision-Language Models,** CVPR 2022 (arXiv:2203.05557).    *Kaiyang Zhou, Jingkang Yang, Chen Change Loy, Ziwei Liu.* [[Paper](https://arxiv.org/abs/2203.05557)][[Code](https://github.com/KaiyangZhou/CoOp)] - **Visual Prompt Tuning,** ECCV 2022 (arXiv:2203.12119).    *Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, Ser-Nam Lim.* [[Paper](https://arxiv.org/abs/2203.12119)][[Code](https://github.com/kmnp/vpt)] - **Exploring Visual Prompts for Adapting Large-Scale Models,** arXiv:2203.17274.   *Hyojin Bahng, Ali Jahanian, Swami Sankaranar
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matched fp:4308b7398779e225, topic:computer-vision, desc:computer vision, readme:computer vision
matched fp:4308b7398779e225, topic:deep-learning, readme:pre-training
matched fp:4308b7398779e225, readme:fine-tuning, readme:fine tuning, name:parameter-efficient