Class-incremental semantic image segmentation assumes multiple model updates, each enriching the model to segment new categories. This is typically carried out by providing expensive pixel-level annotations to the training algorithm for all new objects, limiting the adoption of such methods in practical applications. Approaches that solely require image-level labels offer an attractive alternative, yet, such coarse annotations lack precise information about the location and boundary of the new objects. In this paper we argue that, since classes represent not just indices but semantic entities, the conceptual relationships between them can provide valuable information that should be leveraged. We propose a weakly supervised approach that exploits such semantic relations to transfer objectness prior from the previously learned classes into the new ones, complementing the supervisory signal from image-level labels. We validate our approach on a number of continual learning tasks, and show how even a simple pairwise interaction between classes can significantly improve the segmentation mask quality of both old and new classes. We show these conclusions still hold for longer and, hence, more realistic sequences of tasks and for a challenging few-shot scenario.

翻译：类增量式语义图像分割假设模型需进行多次更新，每次更新使模型能够分割新类别。这一过程通常要求为所有新目标提供昂贵的像素级标注以训练算法，限制了此类方法在实际应用中的推广。仅需图像级标签的方法虽然颇具吸引力，但这类粗粒度标注缺乏新目标位置与边界的精确信息。本文提出：由于类别不仅代表索引，更代表语义实体，类别间的概念关系可提供有价值的信息，应当加以利用。我们提出一种弱监督方法，通过利用语义关系将先前学习类别的目标性先验迁移至新类别，从而补充图像级标签的监督信号。我们在多个持续学习任务上验证了该方法，结果表明，即使仅利用类别间的简单成对交互，也能显著提升新旧类别的分割掩码质量。我们进一步证明：在更长（即更贴合实际的多任务序列）以及更具挑战性的少样本场景中，上述结论依然成立。