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Synthetic datasets have successfully been used to probe visualquestion-answering datasets for their reasoning abilities. CLEVR(johnson2017clevr), for example, tests a range of visual reasoning abilities.The questions in CLEVR focus on comparisons of shapes, colors, and sizes,numerical reasoning, and existence claims. This paper introduces a minimallybiased, diagnostic visual question-answering dataset, QLEVR, that goes beyondexistential and numerical quantification and focus on more complex quantifiersand their combinations, e.g., asking whether there are more than two red ballsthat are smaller than at least three blue balls in an image. We describe howthe dataset was created and present a first evaluation of state-of-the-artvisual question-answering models, showing that QLEVR presents a formidablechallenge to our current models. Code and Dataset are available athttps://github.com/zechenli03/QLEVR

QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning