论文标题
特征：通过学习功能嵌入的非最大抑制作用
FeatureNMS: Non-Maximum Suppression by Learning Feature Embeddings
论文作者
论文摘要
大多数最先进的对象检测器输出每个对象的多个检测。在称为非最大抑制的后处理步骤中删除了重复项。经典的非最大抑制在包含具有高重叠的对象的场景中存在缺点：这种启发式假定两个边界框之间的高重叠与一个重复的高概率相对应。我们提出了解决此问题的功能。特征元素不仅基于边界框之间的联合交集，还基于特征向量的差异来识别重复。这些特征向量可以编码更多信息，例如视觉外观。我们的方法的表现优于经典的NM，衍生的方法和实现了最先进的表现。
Most state of the art object detectors output multiple detections per object. The duplicates are removed in a post-processing step called Non-Maximum Suppression. Classical Non-Maximum Suppression has shortcomings in scenes that contain objects with high overlap: This heuristic assumes that a high overlap between two bounding boxes corresponds to a high probability of one being a duplicate. We propose FeatureNMS to solve this problem. FeatureNMS recognizes duplicates not only based on the intersection over union between the bounding boxes, but also based on the difference of feature vectors. These feature vectors can encode more information like visual appearance. Our approach outperforms classical NMS and derived approaches and achieves state of the art performance.
