Deep Exemplar Networks for VQA and VQG
TABLE VI: VQA2.0 accuracy on Validation set for DCN and DAN
['[BOLD] Models', '[BOLD] All', '[BOLD] Yes/No', '[BOLD] Number', '[BOLD] others']
We next evaluate the proposed method on a recently proposed VQA-2 dataset [Goyal_CVPR2017]. The aim of this new dataset is to remove the bias in different questions. It is a more challenging dataset as compared to the previous VQA-1 dataset [VQA]. We provide a comparison of the proposed DAN and DCN methods against the stacked attention network (SAN) [Yang_CVPR2016] method. We observe that our proposed methods are also able to improve the result over the SAN method. DCN with 4 nearest neighbors, when combined with MCB, obtains an accuracy of 65.90%.