Continual semantic segmentation aims to learn new classes while maintaining the information from the previous classes. Although prior studies have shown impressive progress in recent years, the fairness concern in the continual semantic segmentation needs to be better addressed. Meanwhile, fairness is one of the most vital factors in deploying the deep learning model, especially in human-related or safety applications. In this paper, we present a novel Fairness Continual Learning approach to the semantic segmentation problem. In particular, under the fairness objective, a new fairness continual learning framework is proposed based on class distributions. Then, a novel Prototypical Contrastive Clustering loss is proposed to address the significant challenges in continual learning, i.e., catastrophic forgetting and background shift. Our proposed loss has also been proven as a novel, generalized learning paradigm of knowledge distillation commonly used in continual learning. Moreover, the proposed Conditional Structural Consistency loss further regularized the structural constraint of the predicted segmentation. Our proposed approach has achieved State-of-the-Art performance on three standard scene understanding benchmarks, i.e., ADE20K, Cityscapes, and Pascal VOC, and promoted the fairness of the segmentation model.

翻译：持续语义分割旨在学习新类别的同时保持对旧类别信息的记忆。尽管近年来的研究已取得显著进展，但持续语义分割中的公平性问题仍亟待解决。与此同时，公平性作为深度学习模型部署的最关键因素之一，在人机交互或安全相关应用中尤为突出。本文提出了一种新颖的公平持续学习方法以解决语义分割问题。具体而言，基于公平性目标，我们提出了一种基于类别分布的公平持续学习框架。随后，针对持续学习中的主要挑战——灾难性遗忘与背景偏移，我们创新性地提出了原型对比聚类损失函数。该损失函数被证明是持续学习中常用知识蒸馏范式的一种新颖且泛化的学习模式。此外，所提出的条件结构一致性损失进一步规范了预测分割结果的结构约束。我们提出的方法在ADE20K、Cityscapes和Pascal VOC三个标准场景理解基准测试中均取得了最先进的性能，并有效提升了分割模型的公平性。