Visual Question Answering With Memory-Augmented Networks
Chao Ma, Chunhua Shen, Anthony Dick, Qi Wu, Peng Wang, Anton van den Hengel, Ian Reid
Abstract
In this paper, we exploit memory-augmented neural networks to predict accurate answers to visual questions, even when those answers rarely occur in the training set. The memory network incorporates both internal and external memory blocks and selectively pays attention to each training exemplar. We show that memory-augmented neural networks are able to maintain a relatively long-term memory of scarce training exemplars, which is important for visual question answering due to the heavy-tailed distribution of answers in a general VQA setting. Experimental results in two large-scale benchmark datasets show the favorable performance of the proposed algorithm with the comparison to state of the art.
BibTeX
@inproceedings{cvpr2018_visualquestionan,
title = {Visual Question Answering With Memory-Augmented Networks},
author = {Chao Ma and Chunhua Shen and Anthony Dick and Qi Wu and Peng Wang and Anton van den Hengel and Ian Reid},
booktitle = {CVPR 2018},
year = {2018}
}