https://github.com/kdexd/probnmn-clevr

Code for ICML 2019 paper "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering" [long-oral]
https://github.com/kdexd/probnmn-clevr

clevr icml icml-2019 neural-module-networks probabilistic-models vqa

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Code for ICML 2019 paper "Probabilistic Neural-symbolic Models for Interpretable Visual Question Answering" [long-oral]

• Host: GitHub
• URL: https://github.com/kdexd/probnmn-clevr
• Owner: kdexd
• License: mit
• Created: 2019-05-14T13:35:08.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2022-06-21T21:58:19.000Z (over 2 years ago)
• Last Synced: 2023-02-28T22:51:53.206Z (over 1 year ago)
• Topics: clevr, icml, icml-2019, neural-module-networks, probabilistic-models, vqa
• Language: Python
• Homepage: https://kdexd.github.io/probnmn-clevr
• Size: 1.6 MB
• Stars: 63
• Watchers: 3
• Forks: 11
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
Probabilistic Neural-symbolic Models

====================================

Code for our ICML 2019 paper:

**[Probabilistic Neural-Symbolic Models for Interpretable Visual Question Answering][1]**  

Ramakrishna Vedantam, Karan Desai, Stefan Lee, Marcus Rohrbach, Dhruv Batra, Devi Parikh

Checkout our package documentation at

[kdexd.github.io/probnmn-clevr](https://kdexd.github.io/probnmn-clevr)!

![probnmn-model](docs/_static/probnmn_model.jpg)

If you find this code useful, please consider citing:

```text

@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

------------------

1. [How to setup this codebase?][2]

2. [How to train your ProbNMN?][3]

3. [How to evaluate or run inference?][4]

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](https://github.com/kdexd/probnmn-clevr/releases)!

Acknowledgments

---------------

We thank the developers of:

1. [@davidmascharka/tbd-nets](https://www.github.com/davidmascharka/tbd-nets)

   for providing a very clean implementation of our core Neural Module Network.

2. [@allenai/allennlp](https://www.github.com/allenai/allennlp) for providing

   an awesome framework which indeed takes _masking and padding seriously._ 

3. [@rbgirshick/yacs](https://www.github.com/rbgirshick/yacs) for providing an

   efficient package-wide configuration management.

4. [@pytorch/pytorch](https://www.github.com/pytorch/pytorch), this needs no

   reason.

[1]: https://arxiv.org/abs/1902.07864

[2]: https://kdexd.github.io/probnmn-clevr/probnmn/usage/setup_dependencies.html

[3]: https://kdexd.github.io/probnmn-clevr/probnmn/usage/training.html

[4]: https://kdexd.github.io/probnmn-clevr/probnmn/usage/evaluation_inference.html