SAILS: 通过增量学习语义进行任务不变和无训练的持续学习
SAILS: Segment Anything with Incrementally Learned Semantics for Task-Invariant and Training-Free Continual Learning
AI总结：
SAILS通过增量学习语义实现任务不变和无训练的持续学习，无需增量训练即可超越传统方法，有效解决遗忘问题并提升任务间性能
摘要：
持续学习仍然受到重复重新训练、高计算成本以及遗忘问题的限制。这些因素显著限制了持续学习在现实世界中的应用，因为迭代模型更新需要大量计算资源，并且本质上加剧了遗忘。我们提出了SAILS--通过增量学习语义进行持续学习（CISS），这是一种无训练框架，完全规避了这些挑战。SAILS利用基础模型将CISS分解为两个阶段：使用Segment Anything Model（SAM）进行零样本区域提取，随后通过固定特征空间中的原型进行语义关联。SAILS引入了选择性类内聚类，导致每个类别有多个原型，以更好地建模类内变化。我们的结果表明，尽管不需要增量训练，SAILS通常在标准CISS数据集上超越现有的基于训练的方法，特别是在长且具有挑战性的任务序列中，遗忘问题最严重的情况下。通过避免参数更新，SAILS完全消除了遗忘并保持一致的任务不变性能。此外，SAILS表现出积极的反向转移，新类别的引入可以增强先前类别的性能。
英文摘要：
Continual learning remains constrained by the need for repeated retraining, high computational costs, and the persistent challenge of forgetting. These factors significantly limit the applicability of continuous learning in real-world settings, as iterative model updates require significant computational resources and inherently exacerbate forgetting. We present SAILS -- Segment Anything with Incrementally Learned Semantics, a training-free framework for Class-Incremental Semantic Segmentation (CISS) that sidesteps these challenges entirely. SAILS leverages foundational models to decouple CISS into two stages: Zero-shot region extraction using Segment Anything Model (SAM), followed by semantic association through prototypes in a fixed feature space. SAILS incorporates selective intra-class clustering, resulting in multiple prototypes per class to better model intra-class variability. Our results demonstrate that, despite requiring no incremental training, SAILS typically surpasses the performance of existing training-based approaches on standard CISS datasets, particularly in long and challenging task sequences where forgetting tends to be most severe. By avoiding parameter updates, SAILS completely eliminates forgetting and maintains consistent, task-invariant performance. Furthermore, SAILS exhibits positive backward transfer, where the introduction of new classes can enhance performance on previous classes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
