Orthogonal Knowledge Refreshing for Domain-Incremental Object Detection

Domain-incremental object detection (DIOD) requires mod-els to continually adapt to new domains while preserving prior knowl-edge. Recently, parameter-efficient fine-tuning offers a promising avenue,wherein a pre-trained model is frozen and a small number of learnable pa-rameters are injected for downstream tasks. However, these methods riskoverwriting critical past knowledge, triggering inter-domain interferenceand performance degradation. To address this challenge, we propose Or-thogonal Knowledge Refreshing (OKR), a simple yet effective frameworkfor DIOD. OKR incrementally constructs independent domain-specificsubspaces via dedicated low-rank branches for each domain, which areseamlessly fused for a holistic decision, enabling conflict-free capacity ex-pansion without domain selection during inference. To minimize knowl-edge interference during fusion, we present a gradient-based orthogo-nal refreshing strategy that projects gradient updates of new domainsonto the orthogonal complement of the fused historical subspace, sup-porting continual adaptation without forgetting. Moreover, to mitigatesemantic fragmentation across domains, we enforce topology-aware con-sistency, aligning the semantic structures of old and new domains. Ex-tensive experiments validate the superiority of OKR, outperforming thebest exemplar-free method by significant margins of +5.6% and +6.5%mAP on the Pascal VOC and BDD100K series, respectively.