This dataset <cite>[7]</cite> consists of about 121K (98K) images, 438K (220K) questions, and 4.4M (2.2M) answers in the train (test) set; and it is created so that the distribution of the answers between train and test splits differ, and hence the models cannot excessively rely on the language prior <cite>[7]</cite> . As expected, <cite>[7]</cite> show that performance of all Visual QA approaches they tested drops significantly between train to test sets.