  Humans recognize the visual world at multiple levels: we effortlessly
categorize scenes and detect objects inside, while also identifying the
textures and surfaces of the objects along with their different compositional
parts. In this paper, we study a new task called Unified Perceptual Parsing,
which requires the machine vision systems to recognize as many visual concepts
as possible from a given image. A multi-task framework called UPerNet and a
training strategy are developed to learn from heterogeneous image annotations.
We benchmark our framework on Unified Perceptual Parsing and show that it is
able to effectively segment a wide range of concepts from images. The trained
networks are further applied to discover visual knowledge in natural scenes.
Models are available at \url{https://github.com/CSAILVision/unifiedparsing}.
