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Nonetheless, stuff classes are\nimportant as they allow to explain important aspects of an image, including (1)\nscene type; (2) which thing classes are likely to be present and their location\n(through contextual reasoning); (3) physical attributes, material types and\ngeometric properties of the scene. To understand stuff and things in context we\nintroduce COCO-Stuff, which augments all 164K images of the COCO 2017 dataset\nwith pixel-wise annotations for 91 stuff classes. We introduce an efficient\nstuff annotation protocol based on superpixels, which leverages the original\nthing annotations. We quantify the speed versus quality trade-off of our\nprotocol and explore the relation between annotation time and boundary\ncomplexity. 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