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Sparse Code Filtering for Action Pattern Mining Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Joint Training of Generic CNN-CRF Models with Stochastic Optimization

Authors : A. Kirillov, D. Schlesinger, S. Zheng, B. Savchynskyy, P. H. S. Torr, C. Rother

Published in: Computer Vision – ACCV 2016

Publisher: Springer International Publishing

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Abstract

We propose a new CNN-CRF end-to-end learning framework, which is based on joint stochastic optimization with respect to both Convolutional Neural Network (CNN) and Conditional Random Field (CRF) parameters. While stochastic gradient descent is a standard technique for CNN training, it was not used for joint models so far. We show that our learning method is (i) general, i.e. it applies to arbitrary CNN and CRF architectures and potential functions; (ii) scalable, i.e. it has a low memory footprint and straightforwardly parallelizes on GPUs; (iii) easy in implementation. Additionally, the unified CNN-CRF optimization approach simplifies a potential hardware implementation. We empirically evaluate our method on the task of semantic labeling of body parts in depth images and show that it compares favorably to competing techniques.

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previous chapter Efficient Model Averaging for Deep Neural Networks
next chapter Object-Aware Dictionary Learning with Deep Features
Footnotes
2
We use the commonly adopted terminology from the CNN literature for technical details, to allow reproducibility of our results.
 
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Metadata
Title
Joint Training of Generic CNN-CRF Models with Stochastic Optimization
Authors
A. Kirillov
D. Schlesinger
S. Zheng
B. Savchynskyy
P. H. S. Torr
C. Rother
Copyright Year
2017
Book
Computer Vision – ACCV 2016

Print ISBN: 978-3-319-54183-9

Electronic ISBN: 978-3-319-54184-6

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-54184-6_14

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