In the field of class incremental learning (CIL), generative replay has become increasingly prominent as a method to mitigate the catastrophic forgetting, alongside the continuous improvements in generative models. However, its application in class incremental object detection (CIOD) has been significantly limited, primarily due to the complexities of scenes involving multiple labels. In this paper, we propose a novel approach called stable diffusion deep generative replay (SDDGR) for CIOD. Our method utilizes a diffusion-based generative model with pre-trained text-to-diffusion networks to generate realistic and diverse synthetic images. SDDGR incorporates an iterative refinement strategy to produce high-quality images encompassing old classes. Additionally, we adopt an L2 knowledge distillation technique to improve the retention of prior knowledge in synthetic images. Furthermore, our approach includes pseudo-labeling for old objects within new task images, preventing misclassification as background elements. Extensive experiments on the COCO 2017 dataset demonstrate that SDDGR significantly outperforms existing algorithms, achieving a new state-of-the-art in various CIOD scenarios. The source code will be made available to the public.

翻译：在类增量学习（CIL）领域，生成重放作为一种缓解灾难性遗忘的方法，随着生成模型的不断改进而日益突出。然而，其在类增量目标检测（CIOD）中的应用受到显著限制，主要原因是涉及多标签场景的复杂性。本文提出了一种针对CIOD的新方法，称为稳定扩散深度生成重放（SDDGR）。该方法利用基于扩散的生成模型，结合预训练的文本到扩散网络，生成逼真且多样化的合成图像。SDDGR采用迭代细化策略，生成包含旧类别的高质量图像。此外，我们引入L2知识蒸馏技术，以提升合成图像对先前知识的保持能力。我们的方法还包括对新任务图像中旧对象的伪标签生成，防止其被误分类为背景元素。在COCO 2017数据集上的大量实验表明，SDDGR显著优于现有算法，在各种CIOD场景中达到了新的最先进水平。源代码将向公众公开。