QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning
This addresses the problem of evaluating visual reasoning abilities in AI models, specifically for complex quantificational language, but it is incremental as it builds on existing synthetic datasets like CLEVR.
The paper introduces QLEVR, a diagnostic visual question-answering dataset that tests complex quantifiers and their combinations, such as 'more than two red balls that are smaller than at least three blue balls', and shows that it presents a formidable challenge to current state-of-the-art models.
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