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Erschienen in:
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

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

verfasst von : Holger Caesar, Jasper Uijlings, Vittorio Ferrari

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

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Abstract

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 \(64.0\,\%\) on SIFT Flow and \(49.9\,\%\) on PASCAL Context, and is particularly accurate at object boundaries.

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Vorheriges Kapitel Gaussian Process Density Counting from Weak Supervision
Nächstes Kapitel Fast 6D Pose Estimation from a Monocular Image Using Hierarchical Pose Trees
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Metadaten
Titel
Region-Based Semantic Segmentation with End-to-End Training
verfasst von
Holger Caesar
Jasper Uijlings
Vittorio Ferrari
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46447-3

Electronic ISBN: 978-3-319-46448-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46448-0_23