发表机构
Pusan National University（釜山国立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对稀疏标注与开放世界目标检测共存的问题，提出SA-OWOD任务，构建DPOD框架，通过KTRM与DDTG模块提升未知目标检测性能，优于现有方法。
AI 中文摘要
真实世界中的目标检测在模糊监督下运行，未标注区域可能对应已知目标的缺失标注或真正的未知类别。这些挑战已分别在稀疏标注目标检测（SAOD）和开放世界目标检测（OWOD）中得到解决，但在实践中，二者的共存仍是一个未解决的问题。为解决该问题，我们提出稀疏标注开放世界目标检测（SA-OWOD），这是一项同时考虑稀疏监督和未知类别存在的新任务。我们提出双视角目标发现（DPOD），这是一个通过两种互补机制联合建模未标注已知和未知实例的统一框架。已知目标恢复模块（KTRM）恢复未标注已知实例的监督，并显式正则化特征空间以分离已知和未知表示；互补地，双分歧目标生成器（DDTG）通过跨视图语义不一致识别可靠的未知候选。通过整合这些模块，DPOD解决了由模糊未标注区域导致的矛盾监督信号，从而防止已知与未知目标间的误分类并稳定决策边界。在稀疏标注开放世界基准上的实验结果表明，所提方法优于现有开放世界检测方法，尤其在检测未知目标方面表现突出。
英文摘要
Real-world object detection operates under ambiguous supervision, where unlabeled regions may correspond to missing annotations of known objects or genuinely unknown categories. These challenges have been addressed separately in Sparsely Annotated Object Detection (SAOD) and Open-World Object Detection (OWOD). In practice, their co-occurrence remains an open problem. To address this problem, we introduce Sparsely Annotated Open-World Object Detection (SA-OWOD), a new task that jointly considers sparse supervision and the presence of unseen categories. We propose Dual-Perspective Object Discovery (DPOD), a unified framework that jointly models unlabeled known and unknown instances via two complementary mechanisms. The Known Target Recovery Module (KTRM) recovers supervision for unlabeled known instances and explicitly regularizes the feature space to separate known and unknown representations. Complementarily, the Dual-Disagreement Target Generator (DDTG) identifies reliable unknown candidates through cross-view semantic inconsistency. By integrating these modules, DPOD resolves contradictory supervision signals caused by ambiguous unlabeled regions. As a result, it prevents misclassification between known and unknown objects and stabilizes the decision boundaries. Experimental results on sparsely annotated open-world benchmarks demonstrate that the proposed method outperforms existing open-world detection methods, particularly in detecting unknown objects.
