Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation

@article{Lin2016EfficientPT,
  title={Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation},
  author={Guosheng Lin and Chunhua Shen and Ian D. Reid and Anton van den Hengel},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={3194-3203}
}
Recent advances in semantic image segmentation have mostly been achieved by training deep convolutional neural networks (CNNs). We show how to improve semantic segmentation through the use of contextual information, specifically, we explore 'patch-patch' context between image regions, and 'patch-background' context. For learning from the patch-patch context, we formulate Conditional Random Fields (CRFs) with CNN-based pairwise potential functions to capture semantic correlations between… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 355 CITATIONS, ESTIMATED 40% COVERAGE

Semantic Segmentation with Reverse Attention

VIEW 11 EXCERPTS
CITES RESULTS, METHODS & BACKGROUND
HIGHLY INFLUENCED

Deep Learning Markov Random Field for Semantic Segmentation

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2018
VIEW 7 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2018
VIEW 9 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

Deep Structured Learning for Facial Action Unit Intensity Estimation

  • 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • 2017
VIEW 13 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Learning Dual Multi-Scale Manifold Ranking for Semantic Segmentation of High-Resolution Images

  • Remote Sensing
  • 2017
VIEW 6 EXCERPTS
CITES METHODS, BACKGROUND & RESULTS
HIGHLY INFLUENCED

Pattern Recognition

  • Lecture Notes in Computer Science
  • 2017
VIEW 9 EXCERPTS
CITES BACKGROUND & METHODS
HIGHLY INFLUENCED

FILTER CITATIONS BY YEAR

2015
2019

CITATION STATISTICS

  • 48 Highly Influenced Citations

  • Averaged 102 Citations per year over the last 3 years

  • 7% Increase in citations per year in 2018 over 2017

References

Publications referenced by this paper.
SHOWING 1-10 OF 48 REFERENCES

BoxSup: Exploiting Bounding Boxes to Supervise Convolutional Networks for Semantic Segmentation

  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

The Role of Context for Object Detection and Semantic Segmentation in the Wild

  • 2014 IEEE Conference on Computer Vision and Pattern Recognition
  • 2014
VIEW 3 EXCERPTS
HIGHLY INFLUENTIAL

Learning Hierarchical Features for Scene Labeling

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2013
VIEW 5 EXCERPTS
HIGHLY INFLUENTIAL

Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields

  • IEEE Transactions on Pattern Analysis and Machine Intelligence
  • 2016
VIEW 2 EXCERPTS

Conditional Random Fields as Recurrent Neural Networks

  • 2015 IEEE International Conference on Computer Vision (ICCV)
  • 2015
VIEW 2 EXCERPTS

Similar Papers

Loading similar papers…