In recent years, a number of VQA datasets have been proposed: VQA 1.0 [4] , VQA-abstract [<cite>1</cite>] , VQA 2.0 [47, 14] , FM-IQA [13] , DAQUAR [24] , COCO-QA [30] , Visual Madlibs [46] , Visual Genome [20] , VizWiz [16] , Visual7W [48] , TDIUC [18] , CLEVR [17] , SHAPES [3] , Visual Reasoning [34] , Embodied QA [7] . What all these resources have in common is the task for which they were designed: Given an image (either real or abstract) and a question in natural language, models are asked to correctly answer the question.