Recently, unsupervised domain adaptive object detection methods have been proposed to address the challenge of detecting objects across different domains without labeled data in the target domain. These methods focus on aligning features either at the image level or the instance level. However, due to the absence of annotations in the target domain, existing approaches encounter challenges such as background noise at the image level and prototype aggregation noise at the instance level. To tackle these issues, we introduce a novel adaptive feature alignment network for cross-domain object detection, comprising two key modules. Firstly, we present an adaptive foreground-aware attention module equipped with a set of learnable part prototypes for image-level alignment. This module dynamically generates foreground attention maps, enabling the model to prioritize foreground features, thus reducing the impact of background noise. Secondly, we propose a class-aware prototype alignment module incorporating an optimal transport algorithm for instance-level alignment. This module mitigates the adverse effects of region–prototype aggregation noise by aligning prototypes with instances based on their semantic similarities. By integrating these two modules, our approach achieves better image-level and instance-level feature alignment. Extensive experiments across three challenging scenarios demonstrate the effectiveness of our method, outperforming state-of-the-art approaches in terms of object detection performance.
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