A causal view of compositional zero-shot recognition

This repository hosts the dataset and source code for the paper "A causal view of compositional zero-shot recognition". Yuval Atzmon, Felix Kreuk, Uri Shalit, Gal Chechik, NeurIPS 2020 (Spotlight)

Code

Setup

Build docker image:

cd docker_cfg
docker build --network=host -t causal_comp -f Dockerfile .
cd ..

Prepare UT Zappos50KUT with TMN split:

To reproduce Zappos50KUT results according to TMN evaluation split, you should prepare the dataset according to taskmodularnets: https://github.com/facebookresearch/taskmodularnets

The Zappos50KUT dataset is for academic, non-commercial use only, released by:

• A. Yu and K. Grauman. "Fine-Grained Visual Comparisons with Local Learning". In CVPR, 2014.
• A. Yu and K. Grauman. "Semantic Jitter: Dense Supervision for Visual Comparisons via Synthetic Images". In ICCV, 2017.

Reproduce Zappos experiment.

Notes:

1. Set DATA_DIR to the directory containing the data.
2. Set SEED to [0..4]
3. Set SOURCE_CODE_DIR to the current project workdir
4. Set OUTPUT_DIR to the directory to save the result

SEED=0  # set seed \in [0..4]
SOURCE_CODE_DIR=$HOME/git/causal_comp_prep/
DATA_DIR=/local_data/zap_tmn # SET HERE THE DATA DIR
DATASET=zap_tmn
OUTPUT_DIR=/tmp/output/causal_comp_${DATASET}__seed${SEED}

# prepare output directory
mkdir -p ${OUTPUT_DIR} 
rm -r ${OUTPUT_DIR}/* 
# run experiment
docker run --net=host -v ${SOURCE_CODE_DIR}:/workspace/causal_comp -v ${DATA_DIR}:/data/zap_tmn -v ${OUTPUT_DIR}:/output --user $(id -u):$(id -g)  --shm-size=1g --ulimit memlock=-1 --ulimit stack=6710886  --rm -it causal_comp /bin/bash -c "cd /workspace/causal_comp/; python experiment_zappos_TMNsplit.py --seed=${SEED} --output_dir=/output --data_dir=/data/zap_tmn --use_wandb=0"

Reproduce AO-CLEVr experiments.

Hyperparams for reproducing the results will be published soon

AO-CLEVr Dataset

AO-CLEVr is a new synthetic-images dataset containing images of "easy" Attribute-Object categories, based on the CLEVr framework (Johnson et al. CVPR 2017). AO-CLEVr has attribute-object pairs created from 8 attributes: { red, purple, yellow, blue, green, cyan, gray, brown } and 3 object shapes {sphere, cube, cylinder}, yielding 24 attribute-object pairs. Each pair consists of 7500 images. Each image has a single object that consists of the attribute-object pair. The object is randomly assigned one of two sizes (small/large), one of two materials (rubber/metallic), a random position, and random lightning according to CLEVr defaults.

The dataset can be accessed from the following url.

Cite the paper

If you use the contents of this project, please cite our paper.

@inproceedings{neurips2020_causal_comp_atzmon,
 author = {Atzmon, Yuval and Kreuk, Felix and Shalit, Uri and Chechik, Gal},
 booktitle = {Advances in Neural Information Processing Systems (NeurIPS)},
 title = {A causal view of compositional zero-shot recognition},
 year = {2020}
}

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