Official implementation for the IJCAI'21 paper.

This repository is built upon the code. Thanks for the code sharing of the authors.

Almost all flags can be set by yourself at utils/config.py! We have another extension paper with the LXMERT as baseline achieves SOTA results of 71.44 on the VQA-CP v2 dataset. You can easily combine this loss with our LXMERT implementation.

• python==3.7.7
• pytorch==1.4.0
• tensorboardX==2.1
• torchvision==0.6.0

First of all, make all the data in the right position according to the utils/config.py!

• Please download the VQA-CP datasets in the original paper.
• The image features can be found at the UpDn repo.
• The pre-trained Glove features can be accessed via GLOVE.

1. process questions and dump dictionary:

   python tools/create_dictionary.py

2. process answers and question types:

   python tools/compute_softscore.py

3. convert image features to h5:

   python tools/detection_features_converter.py 

python main.py --name test-VQA --gpu 0

python main.py --name test-VQA --eval-only

If you want to use this code, please cite our papers as follows:

@Inproceedings{adaVQA,
  author    = {Yangyang Guo, Liqiang Nie, Zhiyong Cheng, Feng Ji, Ji Zhang, Alberto Del Bimbo},
  title     = {AdaVQA: Overcoming Language Priors with Adapted Margin Cosine Loss},
  booktitle = {IJCAI},
  year      = {2021},
}
@article{MMDB,
  author    = {Yangyang Guo and
               Liqiang Nie and
               Harry Cheng and
               Zhiyong Cheng and
               Mohan S. Kankanhalli and
               Alberto Del Bimbo},
  title     = {On Modality Bias Recognition and Reduction},
  journal   = {ACM ToMM},
  year      = {2022},
}