QLEVR：一个定量语言和基本视觉推理的诊断数据集
QLEVR: A Diagnostic Dataset for Quantificational Language and Elementary Visual Reasoning
摘要：
合成数据集已成功地用于探测视觉问答数据集的推理能力。例如，CLEVR（johnson2017clevr）测试了一系列视觉推理能力。CLEVR中的问题集中在形状、颜色和大小的比较、数字推理和存在声明。本文介绍了一个最小偏差的诊断视觉问答数据集QLEVR，它超越了存在量化和数值量化，专注于更复杂的量词及其组合，例如，询问在图像中是否存在比至少三个蓝色球小的两个以上的红色球。我们描述了数据集是如何创建的，并对最先进的视觉问答模型进行了第一次评估，表明QLEVR对我们目前的模型提出了巨大的挑战。代码和数据集可在https://github.com/zechenli03/QLEVR上获得
英文摘要：
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
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
