SCAN++: Enhanced Semantic Conditioned Adaptation for Domain Adaptive Object Detection

Domain Adaptive Object Detection (DAOD) transfers an object detector from the labeled source domain to a novel unlabelled target domain. Recent advances bridge the domain gap by aligning category-agnostic feature distribution and minimizing the domain discrepancy for adapting semantic distribution. Though great success, these methods model domain discrepancy with prototypes within a batch, yielding a biased estimation of domain-level statistics. Moreover, the category-agnostic alignment leads to the disagreement of the cross-domain semantic distribution with inevitable classification errors. To address these two issues, we propose an enhanced Semantic Conditioned AdaptatioN (SCAN++) framework, which leverages unbiased semantics for DAOD. Specifically, in the source domain, we design the conditional kernel to sample Pixel of Interests (PoIs), and aggregate PoIs with a cross-image graph to estimate an unbiased semantic sequence. Conditioned on the semantic sequence, we further update the parameter of the conditional kernel in a semantic conditioned manifestation module, and establish a novel conditional graph in the target domain to model unlabeled semantics. After modeling the semantic distribution in both domains, we integrate the conditional kernel into adversarial alignment to achieve semantic-aware adaptation in a Conditional Kernel guided Alignment (CKA) module. Meanwhile, the Semantic Sequence guided Transport (SST) module is proposed to transfer reliable semantic knowledge to the target domain through solving the cross-domain Optimal Transport (OT) assignment, achieving unbiased adaptation at the semantic level. Comprehensive experiments on four adaptation scenarios demonstrate that SCAN++ achieves state-of-the-art results. The code is available at https://github.com/CityU-AIM-Group/SCAN/tree/SCAN++ .