Unified Perceptual Parsing for Scene Understanding

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 https://github.com/CSAILVision/unifiedparsing.

Full paper on arXiv

Details

arXiv identifier

1807.10221

Published

2018-07-26

Authors

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

Models That Cite This Paper

• Described byupernet-convnext-tiny
• Described byupernet-swin-large