SDDGR: Stable Diffusion-based Deep Generative Replay for Class
  Incremental Object Detection
Junsu Kim Hoseong Cho Jihyeon Kim Yihalem Yimolal Tiruneh Seungryul Baek
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.