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Despite the community's efforts\nin data collection, there are still few image datasets covering a wide range of\nscenes and object categories with dense and detailed annotations for scene\nparsing. In this paper, we introduce and analyze the ADE20K dataset, spanning\ndiverse annotations of scenes, objects, parts of objects, and in some cases\neven parts of parts. A generic network design called Cascade Segmentation\nModule is then proposed to enable the segmentation networks to parse a scene\ninto stuff, objects, and object parts in a cascade. We evaluate the proposed\nmodule integrated within two existing semantic segmentation networks, yielding\nsignificant improvements for scene parsing. We further show that the scene\nparsing networks trained on ADE20K can be applied to a wide variety of scenes\nand objects.","url_abs":"http://arxiv.org/abs/1608.05442v2","url_pdf":"http://arxiv.org/pdf/1608.05442v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 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