Deep Exemplar Networks for VQA and VQG
TABLE IV: Analysis network parameter for DCN
['[BOLD] Models', 'VQA1.0 Open-Ended (test-dev)  [BOLD] All', 'VQA1.0 Open-Ended (test-dev)  [BOLD] Yes/No', 'VQA1.0 Open-Ended (test-dev)  [BOLD] Number', 'VQA1.0 Open-Ended (test-dev)  [BOLD] others', 'HAT val dataset  [BOLD] Rank-correlation']
In the proposed DCN network, we have two different configurations, one where we use the add module (DCN-add) for adding the differential context feature and one where we use the (DCN-mul) multiplication module for adding the differential context feature. We further have a dependency on the number of k-nearest neighbors for the DCN network as well. This is also considered. We next evaluate the effect of using a fixed scaling weight (DCN_v1) for adding the differential context feature against learning a linear scaling weight (DCN_v2) for adding the differential context feature. dataset [VQA] and in correlation with human attention is the version that uses multiplication with learned weights and with 4 nearest neighbors being considered. This results in an improvement of 11% in terms of correlation with human attention and 4.8% improvement in accuracy on the VQA-1 dataset [VQA]. We also observe that incorporating DCN with MCB [Fukui_arXiv2016] further improves the results by 4.5% further on the VQA dataset and results in an improvement of 1.47% improvement in correlation with attention. These configurations are used in comparison with the baselines.