SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection

Understand

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.