Segment new object classes using predictions from related old classes with RaSP
Segment new object classes using predictions from related old classes with RaSP
RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation
arXiv paper abstract https://arxiv.org/abs/2305.19879
arXiv PDF paper https://arxiv.org/pdf/2305.19879.pdf
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
... image-level labels offer an attractive alternative, yet, such coarse annotations lack precise information about the location and boundary of the new objects.
... argue that, since classes represent not just indices but semantic entities, the conceptual relationships between them can provide valuable information that should be leveraged.
... 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.
... show how even a simple pairwise interaction between classes can significantly improve the segmentation mask quality of both old and new classes.
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