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CuMix: Towards Recognizing Unseen Categories in Unseen Domains

This is the official PyTorch code of the method CuMix, introduced in our ECCV 2020 work "Towards Recognizing Unseen Categories in Unseen Domains".

Installation

This version has been tested on:

• PyTorch 1.5.1
• python 3.8.3
• cuda 10.2

To install all the dependencies, please run:

pip install -r requirements.txt

Zero-Shot Learning experiments

For setting up the datasets, please download CUB, AWA1, and SUN from here and FLO from here and unpack them. To download them, you can also use the script download_zsl.sh:

./scripts/download_zsl.sh $ZSL_DESIRED_ROOT

For reproducing the results, just run the experiments given the corresponding dataset configuration. For instance, for CUB:

python -m torch.distributed.launch --nproc_per_node=1 main.py --zsl --target cub --config_file configs/zsl/cub.json --data_root $ZSL_DESIRED_ROOT --name cub_exps_zsl

you can find other examples in scripts/zsl.sh.

N.B. To simplify the release, this code does not include the validation procedure used to obtain the hyperparameters. In case you need to validate them, please DO NOT USE the unseen classes, but the splits given with the datasets.

Domain Generalization experiments

For setting up the dataset, please download the official train/val/test splits of PACS from here, unpacking them. To download them, you can also use the script download_pacs.sh:

./scripts/download_pacs.sh $PACS_DESIRED_ROOT

For reproducing the results, just run the experiments given the corresponding dataset configuration. For instance, for testing with cartoon as target:

python -m torch.distributed.launch --nproc_per_node=1 main.py --dg --target cartoon --config_file configs/dg/dg.json --data_root $PACS_DESIRED_ROOT --name cartoon_exps_dg

you can find other examples in scripts/dg.sh.

ZSL+DG experiments

For setting up the datasets, please download DomainNet from here, using the cleaned version. In the data folder, you can find the class splits (that we defined) and the embeddings used here . To download the data and set up the folder, you can also use the script download_dnet.sh:

./scripts/download_dnet.sh $DNET_DESIRED_ROOT

For reproducing the results, just run the experiments given the corresponding dataset configuration. For instance, for testing with painting as target:

python -m torch.distributed.launch --nproc_per_node=1 main.py --zsl --dg --target painting --config_file configs/zsl+dg/painting.json --data_root $DNET_DESIRED_ROOT --name painting_exps_zsldg

you can find other examples in scripts/zsldg.sh.

References

If you find this code useful, please cite:

@inProceedings{mancini2020towards,
    author = {Mancini, Massimiliano and Akata, Zeynep and Ricci, Elisa and Caputo, Barbara},
    title  = {Towards Recognizing Unseen Categories in Unseen Domains},
    booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
    year      = {2020},
    month     = {August}
}

For the ZSL dataloaders and splits, please consider citing:

@article{xian2018zero,
    title={Zero-shot learning—A comprehensive evaluation of the good, the bad and the ugly},
    author={Xian, Yongqin and Lampert, Christoph H and Schiele, Bernt and Akata, Zeynep},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
    volume={41},
    number={9},
    pages={2251--2265},
    year={2018},
    publisher={IEEE}
}

@inproceedings{xian2018feature,
    title={Feature generating networks for zero-shot learning},
    author={Xian, Yongqin and Lorenz, Tobias and Schiele, Bernt and Akata, Zeynep},
    booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
    pages={5542--5551},
    year={2018}
}

for the DG splits:

@inproceedings{li2017deeper,
    title={Deeper, broader and artier domain generalization},
    author={Li, Da and Yang, Yongxin and Song, Yi-Zhe and Hospedales, Timothy M},
    booktitle={Proceedings of the IEEE international conference on computer vision},
    pages={5542--5550},
    year={2017}
}

and for the embeddings used in the DomainNet experiments:

@article{thong2020open,
    title={Open cross-domain visual search},
    author={Thong, William and Mettes, Pascal and Snoek, Cees GM},
    journal={Computer Vision and Image Understanding},
    pages={103045},
    year={2020},
    publisher={Elsevier}
}