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Region-Based Semantic Segmentation with End-to-End Training

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Original languageEnglish
Title of host publicationComputer Vision -- ECCV 2016
Subtitle of host publication14th European Conference, Amsterdam, The Netherlands, October 11--14, 2016, Proceedings, Part I
EditorsBastian Leibe, Jiri Matas, Nicu Sebe, Max Welling
Place of PublicationCham
PublisherSpringer International Publishing
Number of pages17
ISBN (Electronic)978-3-319-46448-0
ISBN (Print)978-3-319-46447-3
Publication statusPublished - 17 Sep 2016
Event14th European Conference on Computer Vision 2016 - Amsterdam, Netherlands
Duration: 8 Oct 201616 Oct 2016

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing
ISSN (Print)0302-9743


Conference14th European Conference on Computer Vision 2016
Abbreviated titleECCV 2016
Internet address


We propose a novel method for semantic segmentation, the task of labeling each pixel in an image with a semantic class. Our method combines the advantages of the two main competing paradigms. Methods based on region classification offer proper spatial support for appearance measurements, but typically operate in two separate stages, none of which targets pixel labeling performance at the end of the pipeline. More recent fully convolutional methods are capable of end-to-end training for the final pixel labeling, but resort to fixed patches as spatial support. We show how to modify modern region-based approaches to enable end-to-end training for semantic segmentation. This is achieved via a differentiable region-to-pixel layer and a differentiable free-form Region-of-Interest pooling layer. Our method improves the state-of-the-art in terms of class-average accuracy with 64.0%64.0% on SIFT Flow and 49.9%49.9% on PASCAL Context, and is particularly accurate at object boundaries.


14th European Conference on Computer Vision 2016


Amsterdam, Netherlands

Event: Conference

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