Class-incremental semantic segmentation (CSS) requires that a model learn to segment new classes without forgetting how to segment previous ones: this is typically achieved by distilling the current knowledge and incorporating the latest data. However, bypassing iterative distillation by directly transferring outputs of initial classes to the current learning task is not supported in existing class-specific CSS methods. Via Softmax, they enforce dependency between classes and adjust the output distribution at each learning step, resulting in a large probability distribution gap between initial and current tasks. We introduce a simple, yet effective Class Independent Transformation (CIT) that converts the outputs of existing semantic segmentation models into class-independent forms with negligible cost or performance loss. By utilizing class-independent predictions facilitated by CIT, we establish an accumulative distillation framework, ensuring equitable incorporation of all class information. We conduct extensive experiments on various segmentation architectures, including DeepLabV3, Mask2Former, and SegViTv2. Results from these experiments show minimal task forgetting across different datasets, with less than 5% for ADE20K in the most challenging 11 task configurations and less than 1% across all configurations for the PASCAL VOC 2012 dataset.

该研究解决了类增量语义分割中如何在学习新类别的同时避免忘记旧类别的难题。提出了一种简单且有效的类独立转换（CIT）方法，使现有语义分割模型的输出转换为类独立的形式，建立了一个累积蒸馏框架，从而确保所有类别信息的公平融合。实验结果表明，在不同数据集上，任务遗忘率非常低，ADE20K任务配置中低于5%，PASCAL VOC 2012数据集中在所有配置中低于1%。

重新思考具有类独立转换的类增量语义分割