moymix / mullowbivqa

Hadamard Product for Low-rank Bilinear Pooling

Hadamard Product for Low-rank Bilinear Pooling

Multimodal Low-rank Bilinear Attention Networks (MLB) have an efficient attention mechanism by low-rank bilinear pooling for visual question-answering tasks. MLB achieves a new state-of-the-art performance, having a better parsimonious property than previous methods.

This current code can get 65.07 on Open-Ended and 68.89 on Multiple-Choice on test-standard split for the VQA dataset. For an ensemble model, 66.89 and 70.29, resepectively.

Dependencies

• rnn

You can install the dependencies:

luarocks install rnn

Training

Please follow the instruction from VQA_LSTM_CNN for preprocessing. --split 2 option allows to use train+val set to train, and test-dev or test-standard set to evaluate. Set --num_ans to 2000 to reproduce the result.

For question features, you need to use this:

• skip-thoughts
• DPPnet (see 003_skipthoughts_porting)
• make_lookuptable.lua

for image features,

$ th prepro_res.lua -input_json data_train-val_test-dev_2k/data_prepro.json -image_root path_to_image_root -cnn_model path to cnn_model

The pretrained ResNet-152 model and related scripts can be found in fb.resnet.torch.

$ th train.lua

With the default parameter, this will take around 2.6 days on a sinlge NVIDIA Titan X GPU, and will generate the model under model/. For the result of the paper, use -seconds option for answer sampling in Section 5. seconds.json file can be optained using prepro_seconds.lua.

Evaluation

$ th eval.lua

References

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

@inproceedings{Kim2016c,
author = {Kim, Jin-Hwa and On, Kyoung-Woon and Lim, Woosang and Kim, Jeonghee and Ha, Jung-Woo and Zhang, Byoung-Tak},
booktitle = {5th International Conference on Learning Representations},
title = {{Hadamard Product for Low-rank Bilinear Pooling}},
archivePrefix = {arXiv},
arxivId = {1610.04325},
year = {2017}
}

This code uses Torch7 rnn package and its TrimZero module for question embeddings. Notice that following papers:

@article{Leonard2015a,
author = {L{\'{e}}onard, Nicholas and Waghmare, Sagar and Wang, Yang and Kim, Jin-Hwa},
journal = {arXiv preprint arXiv:1511.07889},
title = {{rnn : Recurrent Library for Torch}},
year = {2015}
}
@inproceedings{Kim2016a,
author = {Kim, Jin-Hwa and Kim, Jeonghee and Ha, Jung-Woo and Zhang, Byoung-Tak},
booktitle = {Proceedings of KIIS Spring Conference},
isbn = {2093-4025},
number = {1},
pages = {165--166},
title = {{TrimZero: A Torch Recurrent Module for Efficient Natural Language Processing}},
volume = {26},
year = {2016}
}

License

BSD 3-Clause License

Patent (Pending)

METHOD AND SYSTEM FOR PROCESSING DATA USING ELEMENT-WISE MULTIPLICATION AND MULTIMODAL RESIDUAL LEARNING FOR VISUAL QUESTION-ANSWERING