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Papers/Unified Perceptual Parsing for Scene Understanding

Unified Perceptual Parsing for Scene Understanding

Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun

2018-07-26ECCV 2018 92D Semantic SegmentationScene UnderstandingSemantic Segmentation
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Abstract

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}.

Results

TaskDatasetMetricValueModel
Semantic SegmentationADE20K valmIoU42.66UperNet (ResNet-101)
Semantic SegmentationADE20KValidation mIoU42.66UperNet (ResNet-101)
2D Semantic SegmentationWildScenesmIoU47.3UPerNet (ConvNeXt-L)
10-shot image generationADE20K valmIoU42.66UperNet (ResNet-101)
10-shot image generationADE20KValidation mIoU42.66UperNet (ResNet-101)

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