Learning Visual Knowledge Memory Networks for Visual Question Answering
Table 4: Results on different object-size groups.
['Group', 'Min area', 'Max area', '#Questions', 'MLB Other', 'MLB Number', 'MLB Y./N.', 'MLB All', 'VKMN Other', 'VKMN Number', 'VKMN Y./N.', 'VKMN All']
We preserve the question where there is at least one detected object’s name is contained in the question or VKMN’s answer, which leaves 39146 and out of 107394 questions. We take the matched object name as the target, and further divide questions into 5 equal-size groups according to target object size. For one target with multiple instances in one image, we take the average size as the target size. From the table, we could draw the following conclusions: (1) For both MLB and VKMN, larger targets show higher overall accuracy than smaller targets, especially on the “Yes/No” answer-type; (2) On the “other” answer-type, different object-size groups have almost consistent performance for both MLB and VKMN; (3) VKMN outperforms MLB on the “other” answer-type significantly on all the object-size groups.