Single-Domain Generalized Object Detection by Balancing Domain Diversity and Invariance

Single-domain generalization for object detection (S-DGOD) aims to transfer knowledge from a single source domain to unseen target domains. In recent years, many models have focused primarily on achieving feature invariance to enhance robustness. However, due to the inherent diversity across domains, an excessive emphasis on invariance can cause the model to overlook the actual differences between images. This overemphasis may complicate the training process and lead to a loss of valuable information. To address this issue, we propose the Diversity Invariance Detection Model (DIDM), which focuses on the balance between the diversity of domain-specific and invariance cross domains. Recognizing that domain diversity introduces variations in domain-specific features, we introduce a Diversity Learning Module (DLM). The DLM is designed to preserve the diversity of domain-specific information with proposed feature diversity loss while limiting the category semantics in the features. In addition, to maintain domain invariance, we incorporate a Weighted Aligning Module (WAM), which aligns features without compromising feature diversity. We conducted our model on five distinct datasets, which have illustrated the superior performance and effectiveness of the proposed model.