- This article presents a new approach called Single-DGOD, aimed at improving object detection models that learn from just one source domain while maintaining semantic structures in cross-domain samples.
- The method focuses on overcoming challenges related to limited single-domain data and the neglect of important semantic relationships, which affect model generalization.
- It introduces two main components: a texture-based self-augmentation (TBSA) module that minimizes irrelevant attributes, and a local-global semantic reasoning (LGSR) module that models semantic relationships to enhance the model's performance; experiments show its effectiveness.