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

CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning