KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation

This repository provides pytorch implementation of Known-region-aware Domain Alignment (KRADA) method published in Transactions on Machine Learning Research [TMLR].

Problem Setup: Open-set domain adaptation segmentation (OSDAS)

Suppose that a set of source images with annotations are denoted as $\lbrace X^S, Y^S \rbrace$ where the source label space is $\mathbb{L}^{H\times W}$ and $\mathbb{L}=\lbrace 1, \dots, K \rbrace$ is the category label set with $K$ known classes. The target images $X^T$ are drawn from a different distribution and the target label set has an additional label $K+1$ to denote the unseen classes that do not appear in $\mathbb{L}$. We aim to train a segmentation model $\mathcal{M}$ to accurately classify each pixel in target images $X^T$ into one class of the label set $\lbrace 1,\dots, K, K+1 \rbrace$.

Known-region-aware Domain Alignment (KRADA)

Visualization Results

Prerequisites:

• python = 3.6.12
• torch = 1.7.1
• torchvision = 0.8.2

Reproduce:

• Download the SYNTHIA Dataset.
• Download the Cityscapes Dataset.

Here we provide the code for reproducing the implementations of AdaptSegNet + KRADA, CLAN + KRADA, and FADA + KRADA for the Synthia -> Cityscapes task. You can find the .sh file in each folder to train and evaluate the models. More code details can be found in the original AdaptSegNet, CLAN, and FADA.

Citation

If you think this work is useful to your research, please cite:

@article{zhou2023krada,
   title={KRADA: Known-region-aware Domain Alignment for Open-set Domain Adaptation in Semantic Segmentation},
   author={Zhou, Chenhong and Liu, Feng and Gong, Chen and Zeng, Rongfei and Liu, Tongliang and Cheung, K. William and Han, Bo},
   journal={Transactions of Machine Learning Research}
   year={2023}
  }

References:

[1] AdaptSegNet (Tsai et al., 2018): https://github.com/wasidennis/AdaptSegNet

[2] CLAN (Luo et al., 2019): https://github.com/RoyalVane/CLAN

[3] FADA (Wang et al., 2020): https://github.com/JDAI-CV/FADA

[4] OSBP (Saito et al., 2018b) https://github.com/ksaito-ut/OPDA_BP