Two-in-One Refinement for Interactive Segmentation

Bibtex

@INPROCEEDINGS{majumder-2020-two,
     author = {Majumder, Soumajit and Rai, Abhinav and Khurana, Ansh and Yao, Angela},
      title = {Two-in-One Refinement for Interactive Segmentation},
  booktitle = {Proc. 31st British Machine Vision Conference (BMVC20)},
       year = {2020},
   abstract = {Deep convolutional neural networks are now mainstream for click-based interactive image
               segmentation. Most frameworks refine false negatives and false positive regions via a succession of
               positive and negative clicks placed centrally in these regions. We propose a simple yet intuitive
               two-in-one refinement strategy placing clicks on the boundary of the object of interest. As boundary
               clicks are a strong cue for extracting the object of interest and we find that they are much more
               effective in correcting wrong segmentation masks. In addition, we propose a boundary-aware loss that
               encourages segmentation masks to respect instance boundaries. We place our new refinement scheme and
               loss formulation within a task-specialized segmentation framework and achieve state-of-the-art
               performance on the standard datasets - Berkeley, Pascal VOC 2012, DAVIS, and MS COCO. We exceed
               competing methods by 6.5%, 9.4%, 10.5% and 2.5%, respectively.}
}