SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection This paper proposes a novel approach called Stable Diffusion-based Deep Generative Replay (SDDGR) for class incremental object detection (CIOD). SDDGR utilizes a diffusion-based generative model with pre-trained text-to-image diffusion networks to generate realistic and diverse synthetic images. The method incorporates an iterative refinement strategy to produce high-quality images encompassing old classes and employs L2 knowledge distillation to improve the retention of prior knowledge in synthetic images. Additionally, pseudo-labeling is used to prevent misclassification of old objects within new task images. 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. SDDGR generates images using grounding inputs and prompts that explain complex scenes, including previously learned objects. The pre-trained SD weights are sub-optimal for CIOD, so the method refines image fidelity through iterative refinement via a trained detector. A model is trained using L2 distillation to facilitate effective knowledge transfer from synthetic images to the updated model. Pseudo-labeling is also used to prevent old task objects from being detected as background elements. The SDDGR framework includes a generation process, iterative refinement, pseudo-labeling, and training with synthetic images. The method achieves excellent performance on the COCO dataset, demonstrating state-of-the-art accuracy. The SDDGR approach includes a method to generate images that include previous class objects, a technique for filtering more expressive images, a method for implementing pseudo labeling of the DETR framework, and a training protocol for using synthetic images. The method is evaluated on the COCO 2017 dataset, showing significant improvements in performance compared to existing methods. The results demonstrate that SDDGR effectively mitigates catastrophic forgetting in CIOD by leveraging synthetic data and knowledge distillation. The method is effective in generating high-quality synthetic images and maintaining performance across multiple phases of incremental learning.SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection This paper proposes a novel approach called Stable Diffusion-based Deep Generative Replay (SDDGR) for class incremental object detection (CIOD). SDDGR utilizes a diffusion-based generative model with pre-trained text-to-image diffusion networks to generate realistic and diverse synthetic images. The method incorporates an iterative refinement strategy to produce high-quality images encompassing old classes and employs L2 knowledge distillation to improve the retention of prior knowledge in synthetic images. Additionally, pseudo-labeling is used to prevent misclassification of old objects within new task images. 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. SDDGR generates images using grounding inputs and prompts that explain complex scenes, including previously learned objects. The pre-trained SD weights are sub-optimal for CIOD, so the method refines image fidelity through iterative refinement via a trained detector. A model is trained using L2 distillation to facilitate effective knowledge transfer from synthetic images to the updated model. Pseudo-labeling is also used to prevent old task objects from being detected as background elements. The SDDGR framework includes a generation process, iterative refinement, pseudo-labeling, and training with synthetic images. The method achieves excellent performance on the COCO dataset, demonstrating state-of-the-art accuracy. The SDDGR approach includes a method to generate images that include previous class objects, a technique for filtering more expressive images, a method for implementing pseudo labeling of the DETR framework, and a training protocol for using synthetic images. The method is evaluated on the COCO 2017 dataset, showing significant improvements in performance compared to existing methods. The results demonstrate that SDDGR effectively mitigates catastrophic forgetting in CIOD by leveraging synthetic data and knowledge distillation. The method is effective in generating high-quality synthetic images and maintaining performance across multiple phases of incremental learning.