LOSA: Learnable Online Style Adaptation for Test-Time Domain Adaptive Object Detection

Domain adaptive object detection methods for remote sensing images (RSIs) typically rely on large-scale target-domain data and multiepoch offline adaption training. However, the wide variation in remote sensing conditions makes it difficult to gather sufficient data for every potential target domain, especially for unexpected domains. To address this challenge, we propose learnable online style adaptation (LOSA), a method that enables the source model to adapt effectively to new target-domain styles with low test-time latency. Specifically, LOSA captures target-domain styles using shallow feature channel statistics and predicts style shifts based on channel dependencies to recalibrate target features. Through a coarse-to-fine alignment loss between online target features and precomputed source-domain statistics, LOSA autonomously learns domain-specific style adaptation strategies. By adopting a dynamically optimized high learning rate, LOSA only requires a small number of samples for test-time training, making it suitable for real-time applications. Experimental results across various scenarios, including normal-to-corrupted, cross-band, and sim-to-real adaptation, demonstrate that the proposed method significantly improves cross-domain object detection performance. Moreover, our method can be well generalized to cross-domain image classification tasks.