https://github.com/synbol/Parameter-Efficient-Transfer-Learning-Benchmark

A Unified Parameter-Efficient Transfer Learning Benchmark for Computer Vision Tasks
https://github.com/synbol/Parameter-Efficient-Transfer-Learning-Benchmark

Last synced: about 2 months ago
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A Unified Parameter-Efficient Transfer Learning Benchmark for Computer Vision Tasks

• Host: GitHub
• URL: https://github.com/synbol/Parameter-Efficient-Transfer-Learning-Benchmark
• Owner: synbol
• Created: 2024-02-25T14:34:02.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-04-07T09:07:52.000Z (6 months ago)
• Last Synced: 2024-07-23T05:24:46.403Z (2 months ago)
• Language: Python
• Size: 1.68 MB
• Stars: 215
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## 
𝓥𝓲𝓼𝓾𝓪𝓵 𝓟𝓪𝓻𝓪𝓶𝓮𝓽𝓮𝓻-𝓔𝓯𝓯𝓲𝓬𝓲𝓮𝓷𝓽 𝓣𝓻𝓪𝓷𝓼𝓯𝓮𝓻 𝓛𝓮𝓪𝓻𝓷𝓲𝓷𝓰 𝓑𝓮𝓷𝓬𝓱𝓶𝓪𝓻𝓴





 

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## 🔥  *News* 

- [ ] [2024/04/30] "**VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Understanding**" code will be released.

- [ ] [2024/04/30] "**MmAP: Multi-modal Alignment Prompt for Cross-domain Multi-task Learning**" code will be released.

* ✅ [2024/03/01] "**Visual PEFT Library/Benchmark**" repo is created.

## ⭐  *Citation* 

If you find our survey and repository useful for your research, please cite it below:

```bibtex

@article{xin2024parameter,

  title={Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey},

  author={Xin, Yi and Luo, Siqi and Zhou, Haodi and Du, Junlong and Liu, Xiaohong and Fan, Yue and Li, Qing and Du, Yuntao},

  journal={arXiv preprint arXiv:2402.02242},

  year={2024}

}

@inproceedings{xin2024vmt,

  title={VMT-Adapter: Parameter-Efficient Transfer Learning for Multi-Task Dense Scene Understanding},

  author={Xin, Yi and Du, Junlong and Wang, Qiang and Lin, Zhiwen and Yan, Ke},

  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},

  volume={38},

  number={14},

  pages={16085--16093},

  year={2024}

}

@inproceedings{xin2024mmap,

  title={Mmap: Multi-modal alignment prompt for cross-domain multi-task learning},

  author={Xin, Yi and Du, Junlong and Wang, Qiang and Yan, Ke and Ding, Shouhong},

  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},

  volume={38},

  number={14},

  pages={16076--16084},

  year={2024}

}

```