When building artificial intelligence systems that can reason and answer questions about visual data, we need diagnostic tests to analyze our progress and discover shortcomings. Existing benchmarks for visual question answering can help, but have strong biases that models can exploit to correctly answer questions without reasoning. They also conflate multiple sources of error, making it hard to pinpoint model weaknesses. We present a diagnostic dataset that tests a range of visual reasoning abilities. It contains minimal biases and has detailed annotations describing the kind of reasoning each question requires. We use this dataset to analyze a variety of modern visual reasoning systems, providing novel insights into their abilities and limitations.

通过建立人工智能系统，以便对视觉数据进行推理和回答问题，我们需要有诊断测试来分析进展并发现缺点。该研究提出了一种诊断数据集，测试了各种视觉推理能力，包含最小的偏差并具有详细注释，描述每个问题需要的推理类型，用于分析各种现代视觉推理系统，提供了新的洞见，以及它们的能力和限制。

CLEVR：一个用于组合语言和初级视觉推理的诊断数据集