In the field of class incremental learning (CIL), genera- tive replay has become increasingly prominent as a method to mitigate the catastrophic forgetting, alongside the con- tinuous improvements in generative models. However, its application in class incremental object detection (CIOD) has been significantly limited, primarily due to the com- plexities 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 syn- thetic images. SDDGR incorporates an iterative refinement strategy to produce high-quality images encompassing old classes. Additionally, we adopt an L2 knowledge distilla- tion technique to improve the retention of prior knowledge in synthetic images. Furthermore, our approach includes pseudo-labeling for old objects within new task images, pre- venting misclassification as background elements. Exten- sive 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，生成式重放作为一种缓解灾难性遗忘的方法，伴随着生成模型的持续改进而日益突出。然而，该方法在类增量目标检测（CIOD）中的应用受到显著限制，主要源于多标签场景的复杂性。本文针对CIOD提出一种名为稳定扩散深度生成重放（SDDGR）的新方法。该方法利用基于扩散的生成模型与预训练的文本到扩散网络，生成真实且多样化的合成图像。SDDGR采用迭代细化策略来生成包含旧类别的高质量图像。此外，我们引入L2知识蒸馏技术以增强合成图像中对先前知识的保留。所提方法还包括对新任务图像中的旧对象进行伪标签标注，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，在多种CIOD场景中达到了新的最优性能。源代码将公开提供。