Convolutional feature masking for joint object and stuff segmentation

@article{Dai2015ConvolutionalFM,
  title={Convolutional feature masking for joint object and stuff segmentation},
  author={Jifeng Dai and Kaiming He and Jian Sun},
  journal={2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2015},
  pages={3992-4000}
}
The topic of semantic segmentation has witnessed considerable progress due to the powerful features learned by convolutional neural networks (CNNs) [13]. The current leading approaches for semantic segmentation exploit shape information by extracting CNN features from masked image regions. This strategy introduces artificial boundaries on the images and may impact the quality of the extracted features. Besides, the operations on the raw image domain require to compute thousands of networks on a… CONTINUE READING
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