通过类别分组提升未知物体检测，不降低已知类表现
O1O: Grouping of Known Classes to Identify Unknown Objects as Odd-One-Out
- 将已知类别分组为超类，利用几何相似性识别未知对象
- 在多个开放世界检测任务中未知召回率显著提升，已知类性能不变
- 适合需要稳定识别未知物体的现实场景应用
在开放世界设置下，基于固定已知类别训练的目标检测方法难以识别未知类别物体。现有方法通常采用类无关方式生成候选位置的伪标签作为近似监督，但此类方法多依赖外观特征，且伪标签噪声会降低已知类检测性能。我们发现几何线索有助于提升未知物体召回率。受人类认知启发，提出将已知类别聚类为超类，通过超类内类别相似性，构建奇数出（odd-one-out）评分机制来识别未知类。在多个开放世界检测基准上的实验表明，该方法在所有任务中均显著提升未知召回率，且未牺牲已知类性能，归因于超类带来的特征空间更优划分。
原文摘要 · Abstract (English)
Object detection methods trained on a fixed set of known classes struggle to detect objects of unknown classes in the open-world setting. Current fixes involve adding approximate supervision with pseudo-labels corresponding to candidate locations of objects, typically obtained in a class-agnostic manner. While previous approaches mainly rely on the appearance of objects, we find that geometric cues improve unknown recall. Although additional supervision from pseudo-labels helps to detect unknown objects, it also introduces confusion for known classes. We observed a notable decline in the model's performance for detecting known objects in the presence of noisy pseudo-labels. Drawing inspiration from studies on human cognition, we propose to group known classes into superclasses. By identifying similarities between classes within a superclass, we can identify unknown classes through an odd-one-out scoring mechanism. Our experiments on open-world detection benchmarks demonstrate significant improvements in unknown recall, consistently across all tasks. Crucially, we achieve this without compromising known performance, thanks to better partitioning of the feature space with superclasses.
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