Structured Regression Gradient Boosting

CVPR 2016

doi:10.1109/CVPR.2016.162 /cvpr/2016/diego2016cvpr-structured/

Abstract

We propose a new way to train a structured output prediction model. More specifically, we train nonlinear data terms in a Gaussian Conditional Random Field (GCRF) by a generalized version of gradient boosting. The approach is evaluated on three challenging regression benchmarks: vessel detection, single image depth estimation and image inpainting. These experiments suggest that the proposed boosting framework matches or exceeds the state-of-the-art.

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Cite

Text

Diego and Hamprecht. "Structured Regression Gradient Boosting." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.162

Markdown

[Diego and Hamprecht. "Structured Regression Gradient Boosting." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/diego2016cvpr-structured/) doi:10.1109/CVPR.2016.162

BibTeX

@inproceedings{diego2016cvpr-structured,
  title     = {{Structured Regression Gradient Boosting}},
  author    = {Diego, Ferran and Hamprecht, Fred A.},
  booktitle = {Conference on Computer Vision and Pattern Recognition},
  year      = {2016},
  doi       = {10.1109/CVPR.2016.162},
  url       = {https://mlanthology.org/cvpr/2016/diego2016cvpr-structured/}
}