【摘要】 计算机视觉中的物体检测包括2个目标——对物体的定位和识别。由于现有大多数的物体检测方法都是类别依赖的,因此无法应对开放场景中新类别的检测。注意到检测中定位和识别2个目标从已知类向未知类迁移的难度不同,定位具有更好的普适性,同时受人类在认知未知物体过程中层次关系的启发,提出一种高斯层级损失模型,在物体检测中采用物体类别层次化建模,在学习层级结构中每个类别多维高斯分布的同时,使用KL散度描述类别之间的层级关系,增强已知类到未知类的迁移性,从而提升物体检测方法在开放场景下对未知类的识别能力。实验表明,所提出的方法可以在不损失已知类性能的前提下,提升对未知类的检测能力。更多还原

【Abstract】 Object detection targets both object localization and categorization. Most of existing object detection methods are category-dependent,and can not deal with the detection task in open world with unknown categories. In aware that the difficulties of localization and categorization are different during transfer from known categories to unknown ones,and the localization is more universal than categorization,and inspired by the process of human categorization on unknown objects,we propose a Gaussian hierarchical loss model which applies an hierarchical modeling of object categories in object detection. KL divergence is used to describe the hierarchical relationship among categories while learning all the class multidimensional Gaussian distributions and enhance the transfer ability from the known classes to the unknown classes. Therefore the proposed method can extend the existing object detection methods to unknown categories in open world. Experimental results show that the proposed method can improve the detection ability of unknown categories without losing the performance on known categories.更多还原