Orthogonal Knowledge Refreshing for Domain-Incremental Object Detection
Domain-incremental object detection (DIOD) requires models to continually adapt to new domains while preserving prior knowledge. Recently, parameter-efficient fine-tuning offers a promising avenue, wherein a pre-trained model is frozen and a small number of learnable parameters are injected for downstream tasks. However, these methods risk overwriting critical past knowledge, triggering inter-domain interference and performance degradation. To address this challenge, we propose Orthogonal Knowledge Refreshing (OKR), a simple yet effective framework for DIOD. OKR incrementally constructs independent domain-specific subspaces via dedicated low-rank branches for each domain, which are seamle...
| Authors | Aoting Zhang, Dongbao Yang, Chang Liu et al. |
|---|---|
| Primary category | cs.CV |
| Published | 19 July 2026 |
| Last updated at source | 19 July 2026 |
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