Diffusion-based Source-biased Model for Single Domain Generalized Object Detection

Han Jiang, Wenfei Yang, Tianzhu Zhang, Yongdong Zhang

Abstract:

Single domain generalized object detection aims to train an
object detector on a single source domain and generalize it
to any unseen domain. Although existing approaches based
on data augmentation exhibit promising results, they overlook domain discrepancies across multiple augmented domains, which limits the performance of object detectors.
To tackle these problems, we propose a novel diffusionbased framework, termed SDG-DiffDet, to mitigate the impact of domain gaps on object detectors. The proposed
SDG-DiffDet consists of a memory-guided diffusion module
and a source-guided denoising module. Specifically, in the
memory-guided diffusion module, we design feature statistics memories that mine diverse style information from local
parts to augment source features. The augmented features
further serve as noise in the diffusion process, enabling the
model to capture distribution differences between practical
domain distributions. In the source-guided denoising module, we design a text-guided condition to facilitate distribution transfer from any unseen distribution to source distribution in the denoising process. By combining these two
designs, our proposed SDG-DiffDet effectively models feature augmentation and target-to-source distribution transfer within a unified diffusion framework, thereby enhancing
the detection performance on unseen domains. Extensive
experiments demonstrate that the proposed SDG-DiffDet
achieves state-of-the-art performance across two challenging scenarios.