Mining

For class incremental semanticsegmentation, suchaphenomenon oftenbecomesmuchworseduetothe background shift,i.e., some concepts learned at previous stages are assigned to the background class at the current training stage, therefore, significantly reducing the performance of these old concepts. To address this issue, we propose a simple yet effective method in this paper, namedMining unseenClasses via RegionalObjectness forSegmentation (MicroSeg).