Learning Unknowns Without Forgetting Knowns: Compositional and Bidirectional Low-Rank Adaptive Open-World Detection Transformer

Open-World Object Detection (OWOD) aims to detect unseen objects as “unknown” while incrementally learning them without catastrophic forgetting. This problem presents two major challenges: (1) the lack of annotations for unknown objects during training, and (2) the risk of catastrophic forgetting during model updates. To address these issues, we propose the COmpositional and Bidirectional low-Rank Adaptive open-world detection transformer (COBRA)-a novel framework built upon a pre-trained Deformable DETR model. Specifically, COBRA first employs an attentional filtering mechanism that prunes previously known (P-Known) and currently known (C-Known) objects, yielding a purified set of candidateunknowns. To system-atically pseudo-label theseunknowns, we introduce a Primitive Composition Recognition (PCR) module, which evaluates set-level similarity between candidate objects and learned primitives, enabling accurate labeling ofpseudo-unknowns. To mitigate catastrophic forgetting during incremental updates, COBRA leverages Bidirectional Low-Rank Adaptation (Bi-LoRA)-a parameter-efficient mechanism that supports forward knowledge transfer and stable backward integration. Together, these components form a synergistic pipeline for continual object discovery and knowledge consolidation. Extensive experiments on MS COCO and PASCAL VOC demonstrate that our rehearsal-free COBRA framework outperforms SAM-powered methods in unknown recall while achieving lower forgetting compared to rehearsal-based competitors.