The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions

Peng Wang, Qi Wu, Chunhua Shen, Anton van den Hengel; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 1173-1182

Abstract


One of the most intriguing features of the Visual Question Answering (VQA) challenge is the unpredictability of the questions. Extracting the information required to answer them demands a variety of image operations from detection and counting, to segmentation and reconstruction. To train a method to perform even one of these operations accurately from image,question,answer tuples would be challenging, but to aim to achieve them all with a limited set of such training data seems ambitious at best. Our method thus learns how to exploit a set of external off-the-shelf algorithms to achieve its goal, an approach that has something in common with the Neural Turing Machine. The core of our proposed method is a new co-attention model. In addition, the proposed approach generates human-readable reasons for its decision, and can still be trained end-to-end without ground truth reasons being given. We demonstrate the effectiveness on two publicly available datasets, Visual Genome and VQA, and show that it produces the state-of-the-art results in both cases.

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[bibtex]
@InProceedings{Wang_2017_CVPR,
author = {Wang, Peng and Wu, Qi and Shen, Chunhua and van den Hengel, Anton},
title = {The VQA-Machine: Learning How to Use Existing Vision Algorithms to Answer New Questions},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {July},
year = {2017}
}