AI 中文总结
该研究针对开放世界目标检测的数据稀缺挑战，提出类别几何监督（CGS）框架，通过保留类别间关系结构提升样本效率，在多任务场景中验证了其有效性。
AI 中文摘要
开放世界目标检测要求模型能够识别已知类别、拒绝陌生对象，并随时间整合新类别，这在数据稀缺场景中尤为具有挑战性，例如生物医学和科学成像领域，其中稀有类别可能仅有少量标注样本，且细粒度类别间仅存在细微形态差异。基于原型的检测器天然适用于这类场景，但它们通常将类别原型学习为独立锚点，忽略了类别间的关系结构。我们提出类别几何监督（CGS），这是一种通用框架，用于约束学习到的原型或类别表示空间，以保留从训练数据中估计的视觉或语义类别差异。CGS引入了保留差异的目标函数，该函数将学习到的类别表示间的成对距离与目标类别几何矩阵对齐，同时保留标准任务损失。我们将同一目标函数实例化应用于原型识别、少样本生物医学目标检测、开放集检测、新类别插入以及COCO上的OWOD适配。实验表明，CGS提升了识别和OVA检测的样本效率，显著增强了新类别插入能力，并在保留大部分已知类别检测性能的同时，提高了COCO上的未知召回率。 ablation实验显示，有意义的视觉几何提供了最可靠的增益，而随机几何虽有助于新类别分离，但对少样本检测的效果一致性较差。这些结果表明，关系类别几何是在有限监督下构建可校准、可扩展的开放世界检测器的有效监督信号。
英文摘要
Open-world object detection requires models to recognize known categories, reject unfamiliar objects, and incorporate new classes over time. This is especially challenging in scarce-data settings such as biomedical and scientific imaging, where rare categories may have only a few annotated examples and fine-grained classes differ by subtle morphology. Prototype-based detectors are natural for this regime, but they typically learn class prototypes as independent anchors, ignoring relational structure among classes. We propose class-geometry supervision (CGS), a general framework that constrains learned prototype or class-representation spaces to preserve visual or semantic class dissimilarities estimated from training data. CGS introduces a dissimilarity-preserving objective that aligns pairwise distances among learned class representations with a target class-geometry matrix while retaining the standard task loss. We instantiate the same objective across prototype recognition, few-shot biomedical object detection, open-set detection, novel-class insertion, and OWOD adaptation on COCO. Experiments show that CGS improves sample efficiency in recognition and ova detection, substantially strengthens novel-class insertion, and improves unknown recall on COCO while retaining much of the known-class detection performance. Ablations show that meaningful visual geometry provides the most reliable gains, while random geometry can help novel separation but is less consistent for few-shot detection. These results suggest that relational class geometry is an effective supervisory signal for building calibrated and extensible open-world detectors under limited supervision.
CommentsUnder review. 12 Pages, 5 figures, 4 tables
