CSDA: Learning Category-Scale Joint Feature for Domain Adaptive Object Detection

Domain Adaptive Object Detection (DAOD) aims to improve the detection performance of target domains by minimizing the feature distribution between the source and target domain. Recent approaches usually align such distributions in terms of categories through adversarial learning and some progress has been made. However, when objects are non-uniformly distributed at different scales, such category-level alignment causes imbalanced object feature learning, refer as the inconsistency of category alignment at different scales. For better category-level feature alignment, we propose a novel DAOD framework of joint category and scale information, dubbed CSDA, such a design enables effective object learning for different scales. Specifically, our framework is implemented by two closely-related modules: 1) SGFF (Scale-Guided Feature Fusion) fuses the category representations of different domains to learn category-specific features, where the features are aligned by discriminators at three scales. 2) SAFE (Scale-Auxiliary Feature Enhancement) encodes scale coordinates into a group of tokens and enhances the representation of category-specific features at different scales by self-attention. Based on the anchor-based Faster-RCNN and anchor-free FCOS detectors, experiments show that our method achieves state-of-the-art results on three DAOD benchmarks.