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We address interactive full image annotation, where the goal is to accuratelysegment all object and stuff regions in an image. We propose an interactive,scribble-based annotation framework which operates on the whole image toproduce segmentations for all regions. This enables sharing scribblecorrections across regions, and allows the annotator to focus on the largesterrors made by the machine across the whole image. To realize this, we adaptMask-RCNN into a fast interactive segmentation framework and introduce aninstance-aware loss measured at the pixel-level in the full image canvas, whichlets predictions for nearby regions properly compete for space. Finally, wecompare to interactive single object segmentation on the COCO panoptic dataset.We demonstrate that our interactive full image segmentation approach leads to a5% IoU gain, reaching 90% IoU at a budget of four extreme clicks and fourcorrective scribbles per region.

Interactive Full Image Segmentation by Considering All Regions Jointly