Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network

@article{Hong2016LearningTK,
  title={Learning Transferrable Knowledge for Semantic Segmentation with Deep Convolutional Neural Network},
  author={Seunghoon Hong and Junhyuk Oh and Honglak Lee and Bohyung Han},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={3204-3212}
}
  • Seunghoon Hong, Junhyuk Oh, +1 author Bohyung Han
  • Published 2016
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
  • We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations available for different categories to guide segmentations on images with only image-level class labels. To make segmentation knowledge transferrable across categories, we design a decoupled encoder-decoder architecture with attention model. In this architecture, the… CONTINUE READING

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