Bilinear attention networks for visual question answering
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README.md

Bilinear attention networks for visual question answering

This repository is the implementation of Bilinear Attention Networks for the visual question answering task. Our single model achieved 70.35 and an ensemble of 15 models achieved 71.84 (Test-standard, VQA 2.0). For the detail, please refer to our technical report.

This repository is based on and inspired by @hengyuan-hu's work. We sincerely thank for their sharing of the codes.

Overview of bilinear attention networks

Prerequisites

You may need a machine with 4 GPUs, 64GB memory, and PyTorch v0.3.1 for Python 3.

  1. Install PyTorch with CUDA and Python 3.5.
  2. Install h5py.

Preprocessing

Our implementation uses the pretrained features from bottom-up-attention, the adaptive 10-100 features per image. In addition to this, the GloVe vectors. For the simplicity, the below script helps you to avoid a hassle.

All data should be downloaded to a data/ directory in the root directory of this repository.

The easiest way to download the data is to run the provided script tools/download.sh from the repository root. If the script does not work, it should be easy to examine the script and modify the steps outlined in it according to your needs. Then run tools/process.sh from the repository root to process the data to the correct format.

For now, you should manually download for the below options (used in our best single model).

We use a part of Visual Genome dataset for data augmentation. The image meta data and the question answers of Version 1.2 are needed to be placed in data/.

We use MS COCO captions to extract semantically connected words for the extended word embeddings along with the questions of VQA 2.0 and Visual Genome. You can download in here. Since the contribution of these captions is minor, you can skip the processing of MS COCO captions by removing cap elements in the target option in this line.

Counting module (Zhang et al., 2018) is integrated in this repository as counting.py for your convenience. The source repository can be found in @Cyanogenoid's vqa-counting.

Training

$ python3 main.py --use_both True --use_vg True

to start training (the options for the train/val splits and Visual Genome to train, respectively). The training and validation scores will be printed every epoch, and the best model will be saved under the directory "saved_models". The default hyperparameters should give you the best result of single model, which is around 70.04 for test-dev split.

Validation

If you trained a model with the training split using

$ python3 main.py

then you can run evaluate.py with appropriate options to evaluate its score for the validation split.

Pretrained model

We provide the pretrained model reported as the best single model in the paper (70.04 for test-dev, 70.35 for test-standard).

Please download the link and move to saved_models/ban/model_epoch12.pth (you may encounter a redirection page to confirm). The training log is found in here.

$ python3 test.py --label mytest

The result json file will be found in the directory results/.

Troubleshooting

Please check troubleshooting wiki and previous issue history.

Citation

If you use this code as part of any published research, we'd really appreciate it if you could cite the following paper:

@article{Kim2018,
author = {Kim, Jin-Hwa and Jun, Jaehyun and Zhang, Byoung-Tak},
journal = {arXiv preprint arXiv:1805.07932},
title = {{Bilinear Attention Networks}},
year = {2018}
}

License

MIT License