Interactive Full Image Segmentation
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We address the task of interactive full image annotation, where the goal is to produce accurate segmentations for all object and stuff regions in an image. To this end we propose an interactive, scribble-based annotation framework which operates on the whole image to produce segmentations for all regions. This enables the annotator to focus on the largest errors made by the machine across the whole image, and to share corrections across nearby regions. Furthermore, we adapt Mask-RCNN into a fast interactive segmentation framework and introduce a new instance-aware loss measured at the pixel-level in the full image canvas, which lets predictions for nearby regions properly compete. Finally, we compare to interactive single object segmentation on the on the COCO panoptic dataset. We demonstrate that, at a budget of four extreme clicks and four corrective scribbles per region, our interactive full image segmentation approach leads to a 5
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