让模型快速学新类又不丢旧知识，提升增量少样本分割能力
Task Consistent Prototype Learning for Incremental Few-shot Semantic Segmentation
- 用元学习模拟增量任务，在基础训练时就学会快速适应
- 在PASCAL和COCO数据集上显著提升增量分割准确率
- 适合研究持续学习与少样本视觉任务的学者参考
增量少样本语义分割（iFSS）要求模型在仅用少量标注样本的情况下，持续学习新类别。传统方法在基础训练阶段（充分学习已有类别）与增量学习阶段（快速适应新类且减少遗忘）目标不一致，导致性能下降。本文提出一种基于元学习的原型学习方法，在基础训练阶段模拟增量评估流程，通过伪增量任务序列和元目标实现快速适应且不遗忘。同时引入原型空间重分布学习，动态调整类原型，优化原型间边界。在基于PASCAL和COCO构建的iFSS数据集上，实验表明该方法显著优于现有方法，为解决iFSS挑战提供了有效思路。
原文摘要 · Abstract (English)
Incremental Few-Shot Semantic Segmentation (iFSS) tackles a task that requires a model to continually expand its segmentation capability on novel classes using only a few annotated examples. Typical incremental approaches encounter a challenge that the objective of the base training phase (fitting base classes with sufficient instances) does not align with the incremental learning phase (rapidly adapting to new classes with less forgetting). This disconnect can result in suboptimal performance in the incremental setting. This study introduces a meta-learning-based prototype approach that encourages the model to learn how to adapt quickly while preserving previous knowledge. Concretely, we mimic the incremental evaluation protocol during the base training session by sampling a sequence of pseudo-incremental tasks. Each task in the simulated sequence is trained using a meta-objective to enable rapid adaptation without forgetting. To enhance discrimination among class prototypes, we introduce prototype space redistribution learning, which dynamically updates class prototypes to establish optimal inter-prototype boundaries within the prototype space. Extensive experiments on iFSS datasets built upon PASCAL and COCO benchmarks show the advanced performance of the proposed approach, offering valuable insights for addressing iFSS challenges.
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