DA-F2F: Domain-Adaptive Object Detection with Feature-to-Feature Modulation and Alignment

Domain adaptive object detection (DAOD) is essential fordeploying reliable detectors in real-world environments with inevitabledomain shifts. While recent approaches rely on image-to-image transla-tion, they often suffer from instability inherent in pixel-level domaintransformation. To address these limitations, we propose DA-F2F, anovel framework that directly aligns domains within the feature space.DA-F2F introduces a style-aware feature modulation (SFM) module thatextracts style statistics from the target domain to dynamically modulatesource representations. Furthermore, to improve adversarial learning, wepropose a soft-weighted proposal alignment (SPA) module that stablyseparates foreground and background regions in the target domain. Ex-tensive experiments demonstrate that DA-F2F significantly outperformsexisting state-of-the-art methods across diverse adaptation scenarios,achieving 60.5% mAP on clear-to-adverse weather (Foggy Cityscapes),46.9% mAP on small-to-large-scale datasets (BDD100K), and 69.9%mAP on synthetic-to-real adaptation (SIM10K). Code is available athttps://github.com/ohhotaek/DA-F2F.