Probabilistic Neural-symbolic Models
Code for our ICML 2019 paper:
Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering
Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh
Checkout our package documentation at kdexd.github.io/probnmn-clevr!
If you find this code useful, please consider citing:
@inproceedings{vedantam2019probabilistic,
title={Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering},
author={Ramakrishna Vedantam and Karan Desai and Stefan Lee and Marcus Rohrbach and Dhruv Batra and Devi Parikh},
booktitle={ICML},
year={2019}
}
Usage Instructions
Pre-trained Checkpoint
Pre-trained checkpoints and corresponding config files (with all the hyper-parameters) for all training phases is available with v1.0 release of this repository. Check out the Releases!
Acknowledgments
We thank the developers of:
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@davidmascharka/tbd-nets for providing a very clean implementation of our core Neural Module Network.
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@allenai/allennlp for providing an awesome framework which indeed takes masking and padding seriously.
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@rbgirshick/yacs for providing an efficient package-wide configuration management.
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@pytorch/pytorch, this needs no reason.