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Pytorch implementation for our NeurIPS 2019 paper "TAB-VCR: Tags and Attributes based VCR Baselines" https://128.84.21.199/abs/1910.14671
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README.md

TAB-VCR: Tags and Attributes based VCR Baselines

This repository contains data and PyTorch code for the paper TAB-VCR: Tags and Attributes based VCR Baselines.

Authors: Jingxiang Lin, Unnat Jain, Alexander G. Swching.

Please check out the project page for more info.

Setting up and using the repo

This repo is based on the VCR dataset repo. The setup process is pretty much the same.

  1. Get the dataset. Follow the steps in data/README.md.

  2. Install cuda 10.0 if it's not available already.

  3. Install anaconda if it's not available already, and create a new environment. You need to install a few things, namely, pytorch 1.2, torchvision (from the layers branch, which has ROI pooling), and allennlp.

wget https://repo.anaconda.com/archive/Anaconda3-5.2.0-Linux-x86_64.sh
conda update -n base -c defaults conda
conda create --name r2c python=3.6
source activate r2c

conda install numpy pyyaml setuptools cmake cffi tqdm pyyaml scipy ipython mkl mkl-include cython typing h5py pandas nltk spacy numpydoc scikit-learn jpeg

conda install pytorch=1.2.0 -c pytorch
pip install git+git://github.com/pytorch/vision.git@24577864e92b72f7066e1ed16e978e873e19d13d

pip install -r allennlp-requirements.txt
pip install --no-deps allennlp==0.8.0
python -m spacy download en_core_web_sm


# this one is optional but it should help make things faster
pip uninstall pillow && CC="cc -mavx2" pip install -U --force-reinstall pillow-simd
  1. Now to set up the environment, run source activate r2c && export PYTHONPATH={path_to_this_repo}.

Train/Evaluate models

Please refer to models/README.md.

Bibtex

@inproceedings{LinNeurIPS2019,
  author = {J. Lin and U. Jain and A.~G. Schwing},
  title = {{TAB-VCR: Tags and Attributes based VCR Baselines}},
  booktitle = {Conference on Neural Information Processing Systems (NeurIPS)},
  year = {2019},
}
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