  Synthetic datasets have successfully been used to probe visual
question-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 minimally
biased, diagnostic visual question-answering dataset, QLEVR, that goes beyond
existential and numerical quantification and focus on more complex quantifiers
and their combinations, e.g., asking whether there are more than two red balls
that are smaller than at least three blue balls in an image. We describe how
the dataset was created and present a first evaluation of state-of-the-art
visual question-answering models, showing that QLEVR presents a formidable
challenge to our current models. Code and Dataset are available at
https://github.com/zechenli03/QLEVR
