V-PETL Bench: A Unified Visual Parameter-Efficient Transfer Learning Benchmark

Yi Xin, Siqi Luo, Xuyang Liu, Yuntao Du, Haodi Zhou +12 more
2/3/2026

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Abstract

Parameter-efficient transfer learning (PETL) methods show promise in adapting a pre-trained model to various downstream tasks while training only a few parameters. In the computer vision (CV) domain, numerous PETL algorithms have been proposed, but their direct employment or comparison remains inconvenient. To address this challenge, we construct a Unified Visual PETL Benchmark (V-PETL Bench) for the CV domain by selecting 30 diverse, challenging

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Cite this paper

@article{xin2026vpetl,
  title  = {V-PETL Bench: A Unified Visual Parameter-Efficient Transfer Learning Benchmark},
  author = {Yi Xin and Siqi Luo and Xuyang Liu and Yuntao Du and Haodi Zhou and Xinyu Cheng and Christina E. Lee and Junlong Du and Haozhe Wang and Mingcai Chen and Ting Liu and Guimin Hu and Zhongwei Wan and Rongchao Zhang and Aoxue Li and Mingyang Yi and Xiaohong Liu},
  year   = {2026},
  doi    = {10.52202/079017-2560},
  url    = {https://doi.org/10.52202/079017-2560},
  journal = {NEURIPS 2024 2024}
}

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