This is the code repository of the paper Open-World Semantic Segmentation Including Class Similarity, accepted to the IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) 2024.
You can find the paper here.
Install the libraries of the requirements.yml, or create a conda environment by conda env create -f requirements.yml and then conda activate openworld.
The weights of ResNet34 with NonBottleneck 1D block pretrained on ImageNet are available here.
You can choose your favourite hyperparameters configuration in args.py. For training, run
python train.py --id <your_id> --dataset_dir <your_data_dir> --num_classes <N> --batch_size 8.
The expected data structure is taken from Cityscapes. BDDAnomaly has been converted to Cityscapes format.
Please cite us at
@inproceedings{sodano2024cvpr,
author = {Matteo Sodano and Federico Magistri and Lucas Nunes and Jens Behley and Cyrill Stachniss},
title = {{Open-World Semantic Segmentation Including Class Similarity}},
booktitle = {{Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR)}},
year = {2024}
}