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 GQA benchmark.
Setup
-
Clone the repo
-
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
- Go to the cloned repo and open a terminal. Download the datasets and embeddings, specifying the desired path (e.g.
DATA_ROOTin 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}
}
117 Dec 28, 2022
25.1k Jan 2, 2023
5 Sep 16, 2022
166 Jan 1, 2023
4 Jul 11, 2022
79 Dec 18, 2022
761 Dec 26, 2022
127 Dec 27, 2022
193 Dec 22, 2022
32 Dec 15, 2022
2.2k Jan 8, 2023
7 Oct 12, 2022
410 Jan 3, 2023
149 Dec 22, 2022
36 Dec 2, 2022
24 Dec 17, 2022
84 Dec 22, 2022
42 Dec 27, 2022
7 Jan 5, 2022