This paper introduces a solid state-of-the-art baseline for a
class-incremental semantic segmentation (CISS) problem. While the recent CISS
algorithms utilize variants of the knowledge distillation (KD) technique to
tackle the problem, they failed to fully address the critical challenges in
CISS causing the catastrophic forgetting; the semantic drift of the background
class and the multi-label prediction issue. To better address these challenges,
we propose a new method, dubbed SSUL-M (Semantic Segmentation with Unknown
Label with Memory), by carefully combining techniques tailored for semantic
segmentation. Specifically, we claim three main contributions. (1) defining
unknown classes within the background class to help to learn future classes
(help plasticity), (2) freezing backbone network and past classifiers with
binary cross-entropy loss and pseudo-labeling to overcome catastrophic
forgetting (help stability), and (3) utilizing tiny exemplar memory for the
first time in CISS to improve both plasticity and stability. The extensively
conducted experiments show the effectiveness of our method, achieving
significantly better performance than the recent state-of-the-art baselines on
the standard benchmark datasets. Furthermore, we justify our contributions with
thorough ablation analyses and discuss different natures of the CISS problem
compared to the traditional class-incremental learning targeting
classification. The official code is available at
this https URL

本文提出了一种新的方法 SSUL-M 方法，该方法结合了针对语义分割的技术并首次使用了微小样本存储器来解决类逐步学习中经常出现的语义漂移和多标签预测问题，实验证明其比其他最新技术基线表现更优秀。

SSUL: 基于示例的类增量学习的未知标签语义分割

SSUL: Semantic Segmentation with Unknown Label for Exemplar-based  Class-Incremental Learning

In class-incremental semantic segmentation, we have no access to the labeled
data of previous tasks. Therefore, when incrementally learning new classes,
deep neural networks suffer from catastrophic forgetting of previously learned
knowledge. To address this problem, we propose to apply a self-training
approach that leverages unlabeled data, which is used for the rehearsal of
previous knowledge. Specifically, we first learn a temporary model for the
current task, and then pseudo labels for the unlabeled data are computed by
fusing information from the old model of the previous task and the current
temporary model. Additionally, conflict reduction is proposed to resolve the
conflicts of pseudo labels generated from both the old and temporary models. We
show that maximizing self-entropy can further improve results by smoothing the
overconfident predictions. Interestingly, in the experiments we show that the
auxiliary data can be different from the training data and that even
general-purpose but diverse auxiliary data can lead to large performance gains.
The experiments demonstrate state-of-the-art results: obtaining a relative gain
of up to 114% on Pascal-VOC 2012 and 8.5% on the more challenging ADE20K
compared to previous state-of-the-art methods.

提出了一种基于自监督学习和伪标签的自训练方法，用于减轻深度神经网络在逐步学习新类任务时出现的灾难性遗忘问题，该方法不仅考虑了历史任务的知识，还利用了额外的数据提高了语义分割的性能。