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We present joint learning of instance and semantic segmentation for visibleand occluded region masks. Sharing the feature extractor with instanceocclusion segmentation, we introduce semantic occlusion segmentation into theinstance segmentation model. This joint learning fuses the instance- andimage-level reasoning of the mask prediction on the different segmentationtasks, which was missing in the previous work of learning instance segmentationonly (instance-only). In the experiments, we evaluated the proposed jointlearning comparing the instance-only learning on the test dataset. We alsoapplied the joint learning model to 2 different types of robotic pick-and-placetasks (random and target picking) and evaluated its effectiveness to achievereal-world robotic tasks.

Joint Learning of Instance and Semantic Segmentation for Robotic Pick-and-Place with Heavy Occlusions in Clutter