- June 2024: Code released for Cityscapes
onelabel. - May 2024: Code released for NYUv2
onelabelandrandomlabels. - May 2024: Website updated with the CVPR poster and video.
- April 2024: Paper website published at kentonishi.com/JTR-CVPR-2024.
First, download the dataset following the instructions in the MTPSL repository.
Code for training JTR is stored in the ./code directory. Some example commands are provided below:
cd code
# NYUv2 onelabel
python train_nyuv2.py \
--data-dir [/some/data/dir] \
--out-dir [/some/output/dir/nyuv2_onelabel] \
--ssl-type onelabel \
--label-dir ./data/nyuv2_settings \
--seg-baseline 25.75 --depth-baseline 0.6511 --norm-baseline 33.73
# NYUv2 randomlabels
python train_nyuv2.py \
--data-dir [/some/data/dir] \
--out-dir [/some/output/dir/nyuv2_randomlabels] \
--ssl-type randomlabels \
--label-dir ./data/nyuv2_settings \
--seg-baseline 27.05 --depth-baseline 0.6626 --norm-baseline 33.58
# Cityscapes onelabel
python train_cityscapes.py \
--data-dir [/some/data/dir] \
--out-dir [/some/output/dir/cityscapes_onelabel] \
--label-dir ./data/cityscapes_settings \
--seg-baseline 69.50 --depth-baseline 0.0186For convenience, we provide a git patch (./code/patches/mtpsl.patch) to modify the MTPSL training code with our dataloader parameters. You can apply the patch as follows:
git clone [email protected]:VICO-UoE/MTPSL.git
cd MTPSL
git apply /path/to/mtpsl.patchAfter applying the patch, you can simply run the commands in the MTPSL repository's README.
If you have any questions, please contact Kento Nishi and Junsik Kim at [email protected] and [email protected].
If you find this code useful, please consider citing our paper:
@misc{nishi2024jointtask,
title={Joint-Task Regularization for Partially Labeled Multi-Task Learning},
author={Kento Nishi and Junsik Kim and Wanhua Li and Hanspeter Pfister},
year={2024},
eprint={2404.01976},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
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