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.

本研究提出了一种名为稳定扩散深度生成重放（SDDGR）的新方法，它利用基于扩散的生成模型和预训练的文本到扩散网络生成逼真多样的合成图像，通过迭代优化策略生成高质量包含旧类别的图像，并采用L2知识蒸馏技术提高合成图像中先前知识的保存，并在新任务图像中利用伪标记技术对旧对象进行预测，防止其被误分类为背景元素。实验结果表明，SDDGR在各种类别增量目标检测场景中显著优于现有算法，达到了最新的技术水平。源代码将向公众提供。

SDDGR: 稳定的基于扩散的深度生成回放用于类别增量目标检测