Class-Incremental learning (CIL) is the ability of artificial agents to accommodate new classes as they appear in a stream. It is particularly interesting in evolving environments where agents have limited access to memory and computational resources. The main challenge of class-incremental learning is catastrophic forgetting, the inability of neural networks to retain past knowledge when learning a new one. Unfortunately, most existing class-incremental object detectors are applied to two-stage algorithms such as Faster-RCNN and rely on rehearsal memory to retain past knowledge. We believe that the current benchmarks are not realistic, and more effort should be dedicated to anchor-free and rehearsal-free object detection. In this context, we propose MultIOD, a class-incremental object detector based on CenterNet. Our main contributions are: (1) we propose a multihead feature pyramid and multihead detection architecture to efficiently separate class representations, (2) we employ transfer learning between classes learned initially and those learned incrementally to tackle catastrophic forgetting, and (3) we use a class-wise non-max-suppression as a post-processing technique to remove redundant boxes. Without bells and whistles, our method outperforms a range of state-of-the-art methods on two Pascal VOC datasets.

翻译：类增量学习（Class-Incremental Learning, CIL）是智能体在数据流中适应新出现类别能力的关键技术，在智能体对内存与计算资源访问受限的动态环境中的研究尤为关键。类增量学习的主要挑战在于灾难性遗忘——神经网络在学习新知识时会丧失对旧知识的保持能力。遗憾的是，现有大多数类增量目标检测方法仍应用于Faster-RCNN等两阶段算法，并依赖重放记忆（rehearsal memory）保留旧知识。我们认为现有基准测试不够真实，应更多致力于无锚框（anchor-free）与免重放（rehearsal-free）的目标检测研究。在此背景下，我们提出了基于CenterNet的类增量目标检测器MultIOD。主要贡献包括：（1）提出多头特征金字塔与多头检测架构以高效分离类别表征；（2）利用初始学习类别与增量学习类别间的迁移学习应对灾难性遗忘；（3）采用类别级非极大值抑制（class-wise non-max-suppression）作为后处理技术去除冗余框。无需复杂技巧，本方法在Pascal VOC的两个数据集上超越了一系列最先进方法。