- 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.