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 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. Results show that our method outperforms a range of state-of-the-art methods on two Pascal VOC datasets, while reducing memory footprint by more than half.

翻译：类增量学习（CIL）是指人工代理能够在一个数据流中整合新类别出现时的能力。这在代理对内存和计算资源有限访问的演化环境中尤为关键。增量学习的主要挑战是灾难性遗忘，即神经网络在学习新知识时无法保留已有知识。遗憾的是，目前大多数用于目标检测的类增量方法都应用于两阶段算法（如Faster-RCNN），并依赖重演记忆来保留过往知识。我们认为这些方法并不现实，应更多关注无锚框和无重演的目标检测。在此背景下，我们提出了MultIOD——一种基于CenterNet的类增量目标检测器。我们的主要贡献包括：（1）提出多头特征金字塔和多头检测架构以高效分离类别表示；（2）在初始学习类与增量学习类之间采用迁移学习以应对灾难性遗忘；（3）采用类别级非极大值抑制作为后处理技术去除冗余边界框。实验结果表明，我们的方法在两个Pascal VOC数据集上优于一系列最先进方法，同时将内存占用减少一半以上。