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Concreteness

An implementation of Quantifying the Visual Concreteness of Words and Topics in Multimodal Datasets with PyTorch.

It uses a ResNet50 along with Spotify's Annoy library to compute the visual concreteness scores of words from MIRFLICKR.

Requirements

To install the basic requirements, run this:

pip install -r requirements.txt

If you'd like use a Jupyter Notebook for interacting with the concreteness scores after computing them, you'll also need:

pip install -r requirements-notebook.txt

As of now, the existing code has only been tested with Python3.6 and Python 3.7.

For the MSCOCO dataset, you'd also have to download the English model for SpaCy by

python -m spacy download en

Usage

Downloading the dataset

Before running, you'll need to download the MIRFLICKR dataset. You can do that with:

cd data
./get_mirflickr.sh

It's 120GB, so it may take a while.

Similarly, you can get the MSCOCO dataset with:

cd data
./get_mscoco.sh

Shell usage

Once your download is finished, you can compute the concreteness scores with:

python main.py -d <mirflickr_directory> -c <cache_directory> -v

Swap in the path to where the mirflickr dataset was downloaded to and a directory of your choice to use for caching.

For the MSCOCO dataset, run with

python main.py -d <mscoco_directory> -c <cache_directory> -v -t mscoco

Jupyter Notebook

If you prefer, you can also run the provided Jupyter Notebook:

jupyter notebook concreteness.ipynb

TODO

  • Improve Jupyter Notebook formatting

Thanks to

@jmhessel for helpful pointers and a great paper.

Citation:

@inproceedings{hessel2018concreteness,
               title={Quantifying the visual concreteness of words and topics in multimodal datasets},
               author={Hessel, Jack and Mimno, David and Lee, Lillian},
               booktitle={NAACL},
               year={2018}
}

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