  Incremental or continual learning has been extensively studied for image
classification tasks to alleviate catastrophic forgetting, a phenomenon that
earlier learned knowledge is forgotten when learning new concepts. For class
incremental semantic segmentation, such a phenomenon often becomes much worse
due to the 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, named Mining
unseen Classes via Regional Objectness for Segmentation (MicroSeg). Our
MicroSeg is based on the assumption that background regions with strong
objectness possibly belong to those concepts in the historical or future
stages. Therefore, to avoid forgetting old knowledge at the current training
stage, our MicroSeg first splits the given image into hundreds of segment
proposals with a proposal generator. Those segment proposals with strong
objectness from the background are then clustered and assigned newly-defined
labels during the optimization. In this way, the distribution characterizes of
old concepts in the feature space could be better perceived, relieving the
catastrophic forgetting caused by the background shift accordingly. Extensive
experiments on Pascal VOC and ADE20K datasets show competitive results with
state-of-the-art, well validating the effectiveness of the proposed MicroSeg.
