English

Learning Rich Image Region Representation for Visual Question Answering

Computer Vision and Pattern Recognition 2019-10-30 v1

Abstract

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.

Keywords

Cite

@article{arxiv.1910.13077,
  title  = {Learning Rich Image Region Representation for Visual Question Answering},
  author = {Bei Liu and Zhicheng Huang and Zhaoyang Zeng and Zheyu Chen and Jianlong Fu},
  journal= {arXiv preprint arXiv:1910.13077},
  year   = {2019}
}

Comments

Rank 2 in VQA Challenge 2019

R2 v1 2026-06-23T11:57:57.752Z