Awesome
OWTTT
This repository is an official implementation for our [ICCV 2023 Oral] paper.
On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion
Yushu Li<sup>1</sup> Xun Xu<sup>2</sup> Yongyi Su<sup>1</sup> Kui Jia<sup>1</sup> <br> <sup>1</sup>South China University of Technology <br><sup>2</sup>Institute for Infocomm Research (I2R), Agency for Science, Technology and Research (A*STAR) <br>
Overview
CIFAR10-C/CIFAR100-C
The code is released in the cifar folder.
ImageNet-C/ImageNet-R
The code is released in the imagenet folder.
Citation
If you find our work useful in your research, please consider citing:
@inproceedings{
li2023robustness,
title={On the Robustness of Open-World Test-Time Training: Self-Training with Dynamic Prototype Expansion},
author={Li, Yushu and Xu, Xun and Su, Yongyi and Jia, Kui},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month={October},
year={2023}
}