SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection
CVPR 2024(2024)
摘要
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.
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