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This work examines the use of a fully convolutional net (FCN) to find animage segment, given a pixel within this segment region. The net receives animage, a point in the image and a region of interest (RoI ) mask. The netoutput is a binary mask of the segment in which the point is located. Theregion where the segment can be found is contained within the input RoI mask.Full image segmentation can be achieved by running this net sequentially,region-by-region on the image, and stitching the output segments into a singlesegmentation map. This simple method addresses two major challenges of imagesegmentation: 1) Segmentation of unknown categories that were not included inthe training set. 2) Segmentation of both individual object instances (things)and non-objects (stuff), such as sky and vegetation. Hence, if the pointerpixel is located within a person in a group, the net will output a mask thatcovers that individual person; if the pointer point is located within the skyregion, the net returns the region of the sky in the image. This is true evenif no example for sky or person appeared in the training set. The net wastested and trained on the COCO panoptic dataset and achieved 67% IOU forsegmentation of familiar classes (that were part of the net training set) and53% IOU for segmentation of unfamiliar classes (that were not included in thetraining).

Class-independent sequential full image segmentation, using a  convolutional net that finds a segment within an attention region, given a  pointer pixel within this segment