Few-shot class-incremental learning (FSCIL) presents the primary challenge of balancing underfitting to a new session's task and forgetting the tasks from previous sessions. To address this challenge, we develop a simple yet powerful learning scheme that integrates effective methods for each core component of the FSCIL network, including the feature extractor, base session classifiers, and incremental session classifiers. In feature extractor training, our goal is to obtain balanced generic representations that benefit both current viewable and unseen or past classes. To achieve this, we propose a balanced supervised contrastive loss that effectively balances these two objectives. In terms of classifiers, we analyze and emphasize the importance of unifying initialization methods for both the base and incremental session classifiers. Our method demonstrates outstanding ability for new task learning and preventing forgetting on CUB200, CIFAR100, and miniImagenet datasets, with significant improvements over previous state-of-the-art methods across diverse metrics. We conduct experiments to analyze the significance and rationale behind our approach and visualize the effectiveness of our representations on new tasks. Furthermore, we conduct diverse ablation studies to analyze the effects of each module.

翻译：少样本类别增量学习（FSCIL）面临的主要挑战是平衡新任务阶段的欠拟合与旧任务阶段的遗忘。为解决这一挑战，我们提出一种简洁而强大的学习方案，该方案整合了FSCIL网络各核心组件的有效方法，包括特征提取器、基础会话分类器和增量会话分类器。在特征提取器训练中，我们的目标是获得平衡的通用表征，使其同时有利于当前可见类、未见类及过往类。为此，我们提出一种平衡监督对比损失函数，有效平衡这两个目标。在分类器方面，我们分析并强调了统一基础会话和增量会话分类器初始化方法的重要性。我们的方法在CUB200、CIFAR100和miniImagenet数据集上展现出卓越的新任务学习能力与抗遗忘性能，在多维度指标上均显著优于现有最优方法。通过实验分析我们方法的有效性与内在机理，并可视化新任务表征的效果。此外，我们开展丰富的消融实验以分析各模块的影响。