Towards Sparsely Annotated Open World Object Detection

Real-world object detection operates under ambiguous su-pervision, where unlabeled regions may correspond to missing annota-tions of known objects or genuinely unknown categories. These chal-lenges have been addressed separately in Sparsely Annotated Object De-tection (SAOD) and Open-World Object Detection (OWOD). In prac-tice, their co-occurrence remains an open problem. To address this prob-lem, we introduce Sparsely Annotated Open-World Object Detection(SA-OWOD), a new task that jointly considers sparse supervision andthe presence of unseen categories. We propose Dual-Perspective ObjectDiscovery (DPOD), a unified framework that jointly models unlabeledknown and unknown instances via two complementary mechanisms. TheKnown Target Recovery Module (KTRM) recovers supervision for unla-beled known instances and explicitly regularizes the feature space to sep-arate known and unknown representations. Complementarily, the Dual-Disagreement Target Generator (DDTG) identifies reliable unknown can-didates through cross-view semantic inconsistency. By integrating thesemodules, DPOD resolves contradictory supervision signals caused byambiguous unlabeled regions. As a result, it prevents misclassificationbetween known and unknown objects and stabilizes the decision bound-aries. Experimental results on sparsely annotated open-world bench-marks demonstrate that the proposed method outperforms existing open-world detection methods, particularly in detecting unknown objects. Thecode is publicly available at: https://github.com/HelloHeeju/SA-OWOD