Instance Segmentation of Indoor Scenes using a Coverage Loss. Silberman, N.; Sontag, D.; and Fergus, R. In Fleet, D. J.; Pajdla, T.; Schiele, B.; and Tuytelaars, T., editors, Proceedings of the 13th European Conference on Computer Vision (ECCV), volume 8689, of Lecture Notes in Computer Science, pages 616–631, 2014. Springer.
Instance Segmentation of Indoor Scenes using a Coverage Loss [pdf]Paper  abstract   bibtex   
A major limitation of existing models for semantic segmentation is the inability to identify individual instances of the same class: when labeling pixels with only semantic classes, a set of pixels with the same label could represent a single object or ten. In this work, we introduce a model to perform both semantic and instance segmentation simultaneously. We introduce a new higher-order loss function that directly minimizes the coverage metric and evaluate a variety of region features, including those from a convolutional network. We apply our model to the NYU Depth V2 dataset, obtaining state of the art results.
@inproceedings{SilSonFer_ECCV14,
  author    = {Nathan Silberman and David Sontag and Rob Fergus},
  title     = {Instance Segmentation of Indoor Scenes using a Coverage Loss},
  booktitle = {Proceedings of the 13th European Conference on Computer Vision (ECCV)},
  series    = {Lecture Notes in Computer Science},
  volume    = {8689},
  publisher = {Springer},
  editor    = {David J. Fleet and
               Tom{\'{a}}s Pajdla and
               Bernt Schiele and
               Tinne Tuytelaars},
  pages     = {616--631},
  year      = {2014},
  keywords = {Computer vision, Machine learning},
  url_Paper = {http://people.csail.mit.edu/dsontag/papers/SilSonFer_ECCV14.pdf},
  abstract = {A major limitation of existing models for semantic segmentation is the inability to identify individual instances of the same class: when labeling pixels with only semantic classes, a set of pixels with the same label could represent a single object or ten. In this work, we introduce a model to perform both semantic and instance segmentation simultaneously. We introduce a new higher-order loss function that directly minimizes the coverage metric and evaluate a variety of region features, including those from a convolutional network. We apply our model to the NYU Depth V2 dataset, obtaining state of the art results.}
}
Downloads: 0