Segment new objects without forgetting old ones by replaying most informative samples with Zhu
Segment new objects without forgetting old ones by replaying most informative samples with Zhu
Continual Semantic Segmentation with Automatic Memory Sample Selection
arXiv paper abstract https://arxiv.org/abs/2304.05015
arXiv PDF paper https://arxiv.org/pdf/2304.05015.pdf
Continual Semantic Segmentation (CSS) extends static semantic segmentation by incrementally introducing new classes for training.
To alleviate the catastrophic forgetting issue in CSS, a memory buffer that stores a small number of samples from the previous classes is constructed for replay.
However, existing methods select the memory samples either randomly or based on a single-factor-driven handcrafted strategy, which has no guarantee to be optimal.
… propose a novel memory sample selection mechanism that selects informative samples for effective replay in a fully automatic way by considering comprehensive factors including sample diversity and class performance.
… mechanism regards the selection operation as a decision-making process and learns an optimal selection policy that directly maximizes the validation performance on a reward set.
… demonstrate the effectiveness of … approach with state-of-the-art (SOTA) performance achieved …
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