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End-to-end Learning for Graph Decomposition

2019

Conference Paper

ps


Deep neural networks provide powerful tools for pattern recognition, while classical graph algorithms are widely used to solve combinatorial problems. In computer vision, many tasks combine elements of both pattern recognition and graph reasoning. In this paper, we study how to connect deep networks with graph decomposition into an end-to-end trainable framework. More specifically, the minimum cost multicut problem is first converted to an unconstrained binary cubic formulation where cycle consistency constraints are incorporated into the objective function. The new optimization problem can be viewed as a Conditional Random Field (CRF) in which the random variables are associated with the binary edge labels. Cycle constraints are introduced into the CRF as high-order potentials. A standard Convolutional Neural Network (CNN) provides the front-end features for the fully differentiable CRF. The parameters of both parts are optimized in an end-to-end manner. The efficacy of the proposed learning algorithm is demonstrated via experiments on clustering MNIST images and on the challenging task of real-world multi-people pose estimation.

Author(s): Jie Song and Bjoern Andres and Michael Black and Otmar Hilliges and Siyu Tang
Book Title: International Conference on Computer Vision
Year: 2019
Month: October

Department(s): Perceiving Systems
Bibtex Type: Conference Paper (inproceedings)
Paper Type: Conference

Event Place: Seoul, South Korea
Attachments: PDF

BibTex

@inproceedings{Jie:graph:decomposition,
  title = {End-to-end Learning for Graph Decomposition},
  author = {Song, Jie and Andres, Bjoern and Black, Michael and Hilliges, Otmar and Tang, Siyu},
  booktitle = {International Conference on Computer Vision},
  month = oct,
  year = {2019},
  month_numeric = {10}
}