Unified Perceptual Parsing for Scene Understanding

Tete Xiao, Yingcheng Liu, Bolei Zhou, Yuning Jiang, Jian Sun; Proceedings of the European Conference on Computer Vision (ECCV), 2018, pp. 418-434


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.

Related Material

[pdf] [arXiv]
author = {Xiao, Tete and Liu, Yingcheng and Zhou, Bolei and Jiang, Yuning and Sun, Jian},
title = {Unified Perceptual Parsing for Scene Understanding},
booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
month = {September},
year = {2018}