Incorporating External Knowledge to Answer Open-Domain Visual Questions with Dynamic Memory Networks
Table 1: Accuracy on Visual7W dataset
['Methods', 'What', 'Where', 'When', 'Who', 'Why', 'How', 'Average']
The overall results demonstrate that our algorithm obtains different boosts compared with the competitors on various kinds of questions, e.g., significant improvements on the questions of Who (5.9%), and What (4.9%) questions, and slightly boost on the questions of When (1.4%) and How (2.0%). After inspecting the success and failure cases, we found that the Who and What questions have larger diversity in questions and multi-choice answers compared to other types, therefore benefit more from external background knowledge.