提出双阶段认知级联模型，解决持续分割中遗忘问题
Zero-Forgetting CISS via Dual-Phase Cognitive Cascades
- 将任务拆分为存在检测与类别分割两阶段，避免知识干扰
- 在PASCAL VOC和ADE20K上实现长序列增量任务显著提升
- 适合需要长期学习新类别的视觉分割场景
持续语义分割（CSS）是计算机视觉中的核心任务，支持众多下游应用，但面临灾难性遗忘挑战。传统基于Softmax分类头的类增量语义分割（CISS）框架中，灾难性遗忘源于任务关联概率的失衡。本文对这些问题进行形式化并提供理论分析，深入揭示现有方法（尤其是严格参数隔离）的局限性。受人类标注者双阶段认知启发，我们提出认知级联分割（CogCaS），一种新的双阶段级联范式，用于CISS设置下的CSS任务。通过将任务解耦为类别存在检测与类别特定分割，CogCaS更有效地实现持续学习，在保留旧知识的同时融入新类别。在两个基准数据集PASCAL VOC 2012和ADE20K上，我们在多种复杂场景中取得显著改进，尤其在长序列增量任务中表现突出。代码将在论文接受后公开。
原文摘要 · Abstract (English)
Continual semantic segmentation (CSS) is a cornerstone task in computer vision that enables a large number of downstream applications, but faces the catastrophic forgetting challenge. In conventional class-incremental semantic segmentation (CISS) frameworks using Softmax-based classification heads, catastrophic forgetting originates from Catastrophic forgetting and task affiliation probability. We formulate these problems and provide a theoretical analysis to more deeply understand the limitations in existing CISS methods, particularly Strict Parameter Isolation (SPI). To address these challenges, we follow a dual-phase intuition from human annotators, and introduce Cognitive Cascade Segmentation (CogCaS), a novel dual-phase cascade formulation for CSS tasks in the CISS setting. By decoupling the task into class-existence detection and class-specific segmentation, CogCaS enables more effective continual learning, preserving previously learned knowledge while incorporating new classes. Using two benchmark datasets PASCAL VOC 2012 and ADE20K, we have shown significant improvements in a variety of challenging scenarios, particularly those with long sequence of incremental tasks, when compared to exsiting state-of-the-art methods. Our code will be made publicly available upon paper acceptance.
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