  We propose to boost VQA by leveraging more powerful feature extractors by
improving the representation ability of both visual and text features and the
ensemble of models. For visual feature, some detection techniques are used to
improve the detector. For text feature, we adopt BERT as the language model and
find that it can significantly improve VQA performance. Our solution won the
second place in the VQA Challenge 2019.
