Awesome

Prompt Distribution Learning (ProDA)

Updates

• 07.11.2022: We will release the code as soon as possible (~few weeks). A raw (uncleaned) code can be downloaded from here (right-click and save).

@InProceedings{Lu2022Prompt,
    author    = {Lu, Yuning and Liu, Jianzhuang and Zhang, Yonggang and Liu, Yajing and Tian, Xinmei},
    title     = {Prompt Distribution Learning},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2022},
    pages     = {5206-5215}
}

This is a PyTorch re-implementation of the CVPR 2022 paper Prompt Distribution Learning (ProDA), reproducing the results on ELEVATER benchmark.

ProDA is the winner of the Parameter-Efficiency track at Image Classification in the Wild (ICinW) Challenge on the ECCV2022 workshop.