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Abstract
It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main difficulties in implementing this in practice are associated with a discrepancy in suitable learning objectives as well as with the necessity of approximations for the inference. In this work we take one of the simplest inference methods, a truncated max-product Belief Propagation, and add what is necessary to make it a proper component of a deep learning model: connect it to learning formulations with losses on marginals and compute the backprop operation. This BP-Layer can be used as the final or an intermediate block in convolutional neural networks (CNNs), allowing us to design a hierarchical model composing BP inference and CNNs at different scale levels. The model is applicable to a range of dense prediction problems, is well-Trainable and provides parameter-efficient and robust solutions in stereo, flow and semantic segmentation.
Original language | English |
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Title of host publication | Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2020 |
Publisher | IEEEXplore |
Pages | 7897-7906 |
Number of pages | 10 |
DOIs | |
Publication status | Published - 14 Jun 2020 |
Event | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition: CVPR 2020 - virtuell, Virtual, United States Duration: 14 Jun 2020 → 19 Jun 2020 |
Conference
Conference | 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition |
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Abbreviated title | CVPR 2020 |
Country/Territory | United States |
City | Virtual |
Period | 14/06/20 → 19/06/20 |
ASJC Scopus subject areas
- Software
- Computer Vision and Pattern Recognition
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Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
Patrick Knöbelreiter (Speaker)
18 Jun 2020Activity: Talk or presentation › Poster presentation › Science to science