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Compositional Zero-Shot Learning

This is the official PyTorch code of the CVPR 2021 works Learning Graph Embeddings for Compositional Zero-shot Learning and Open World Compositional Zero-Shot Learning. The code provides the implementation of the methods CGE, CompCos together with other baselines (e.g. SymNet, AoP, TMN, LabelEmbed+,RedWine). It also provides train and test for the Open World CZSL setting and the new C-GQA benchmark.

Important note: the C-GQA dataset has been updated (see this issue) and the code will automatically download the new version. The results of all models for the updated benchmark can be found in the Co-CGE and KG-SP papers.

Check also:

  • Co-CGE and its repo if you are interested in a stronger OW-CZSL model and a faster OW evaluation code.
  • KG-SP and its repo if you are interested in the partial CZSL setting and a simple but effective OW model.

Setup

  1. Clone the repo

  2. We recommend using Anaconda for environment setup. To create the environment and activate it, please run:

    conda env create --file environment.yml
    conda activate czsl
  1. Go to the cloned repo and open a terminal. Download the datasets and embeddings, specifying the desired path (e.g. DATA_ROOT in the example):
    bash ./utils/download_data.sh DATA_ROOT
    mkdir logs

Training

Closed World. To train a model, the command is simply:

    python train.py --config CONFIG_FILE

where CONFIG_FILE is the path to the configuration file of the model. The folder configs contains configuration files for all methods, i.e. CGE in configs/cge, CompCos in configs/compcos, and the other methods in configs/baselines.

To run CGE on MitStates, the command is just:

    python train.py --config configs/cge/mit.yml

On UT-Zappos, the command is:

    python train.py --config configs/cge/utzappos.yml

Open World. To train CompCos (in the open world scenario) on MitStates, run:

    python train.py --config configs/compcos/mit/compcos.yml

To run experiments in the open world setting for a non-open world method, just add --open_world after the command. E.g. for running SymNet in the open world scenario on MitStates, the command is:

    python train.py --config configs/baselines/mit/symnet.yml --open_world

Note: To create a new config, all the available arguments are indicated in flags.py.

Test

Closed World. To test a model, the code is simple:

    python test.py --logpath LOG_DIR

where LOG_DIR is the directory containing the logs of a model.

Open World. To test a model in the open world setting, run:

    python test.py --logpath LOG_DIR --open_world

To test a CompCos model in the open world setting with hard masking, run:

    python test.py --logpath LOG_DIR_COMPCOS --open_world --hard_masking

References

If you use this code, please cite

@inproceedings{naeem2021learning,
  title={Learning Graph Embeddings for Compositional Zero-shot Learning},
  author={Naeem, MF and Xian, Y and Tombari, F and Akata, Zeynep},
  booktitle={34th IEEE Conference on Computer Vision and Pattern Recognition},
  year={2021},
  organization={IEEE}
}

and

@inproceedings{mancini2021open,
  title={Open World Compositional Zero-Shot Learning},
  author={Mancini, M and Naeem, MF and Xian, Y and Akata, Zeynep},
  booktitle={34th IEEE Conference on Computer Vision and Pattern Recognition},
  year={2021},
  organization={IEEE}
}

Note: Some of the scripts are adapted from AttributeasOperators repository. GCN and GCNII implementations are imported from their respective repositories. If you find those parts useful, please consider citing:

@inproceedings{nagarajan2018attributes,
  title={Attributes as operators: factorizing unseen attribute-object compositions},
  author={Nagarajan, Tushar and Grauman, Kristen},
  booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},
  pages={169--185},
  year={2018}
}