Instance Segmentation of Indoor Scenes using a Coverage Loss. Silberman, N., Sontag, D., & Fergus, R. In Fleet, D. J., Pajdla, T., Schiele, B., & 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.

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