Class-Incremental learning (CIL) refers to the ability of artificial agents to integrate 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 incremental learning is catastrophic forgetting, the inability of neural networks to retain past knowledge when learning a new one. Unfortunately, most existing class-incremental methods for object detection are applied to two-stage algorithms such as Faster-RCNN, and rely on rehearsal memory to retain past knowledge. We argue that those are not suitable in resource-limited environments, and more effort should be dedicated to anchor-free and rehearsal-free object detection. In this paper, we propose MultIOD, a class-incremental object detector based on CenterNet. Our 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. Results show that our method outperforms state-of-the-art methods on two Pascal VOC datasets, while only saving the model in its current state, contrary to other distillation-based counterparts.

翻译：类别增量学习（CIL）指人工智能体在流式数据中整合新类别的能力，在智能体面临内存与计算资源限制的演进环境中尤为重要。增量学习的主要挑战是灾难性遗忘——神经网络在学习新知识时无法保留旧知识。然而，现有大多数目标检测领域的类别增量方法均基于Faster-RCNN等两阶段算法，且依赖重演记忆来保留旧知识。我们认为这些方法不适用于资源受限环境，应更多关注无锚框且无需重演的目标检测方法。本文提出MultIOD——一种基于CenterNet的类别增量目标检测器。我们的贡献包括：（1）提出多头特征金字塔与多头检测架构以有效分离类别表征；（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作为后处理技术移除冗余框。实验结果表明，在仅保存当前模型状态（不同于其他基于蒸馏的方法）的情况下，本方法在Pascal VOC两个数据集上的性能均超越现有最优方法。