Guiding Inter-domain Class Balancing With Salient Features For Domain Adaptive Object Detection

Abstract

Although multi-scale alignment has improved domain adaptive object detection by addressing data distribution differences and annotation challenges, little attention has been given to class distribution differences between domains. Additionally, the utilization of feature information across different alignment levels is limited. To alleviate these issues, this paper proposes a novel domain adaptive method that leverages salient features for inter-domain class balancing. Our method consists of three core modules. Specifically, 1) the pixel feature salience-guided module enhances target focus and guides alignment at other scales, improving overall alignment capability; 2) the spatial domain feature purification module filters the noise, extracts salient features, and provides high-quality samples for alignment; 3) the instance relationship adaptive adjustment module adjusts instance weights for different classes to alleviate class distribution differences between different domains. Extensive experiments on multiple datasets demonstrate the effectiveness of our method.