Generalized few-shot object detection aims to achieve precise detection on both base classes with abundant annotations and novel classes with limited training data. Existing approaches enhance few-shot generalization with the sacrifice of base-class performance, or maintain high precision in base-class detection with limited improvement in novel-class adaptation. In this paper, we point out the reason is insufficient Discriminative feature learning for all of the classes. As such, we propose a new training framework, DiGeo, to learn Geometry-aware features of inter-class separation and intra-class compactness. To guide the separation of feature clusters, we derive an offline simplex equiangular tight frame (ETF) classifier whose weights serve as class centers and are maximally and equally separated. To tighten the cluster for each class, we include adaptive class-specific margins into the classification loss and encourage the features close to the class centers. Experimental studies on two few-shot benchmark datasets (VOC, COCO) and one long-tail dataset (LVIS) demonstrate that, with a single model, our method can effectively improve generalization on novel classes without hurting the detection of base classes.

翻译：广义少样本目标检测旨在对具有丰富标注的基础类和训练数据有限的新类实现精准检测。现有方法往往以牺牲基础类性能为代价来提升少样本泛化能力，或在维持基础类高检测精度的同时，对新类适应性的提升有限。本文指出导致这一问题的根本原因在于对所有类别的判别性特征学习不足。为此，我们提出新型训练框架DiGeo，通过学习具有几何感知能力的特征来增强类间分离性和类内紧凑性。为引导特征簇分离，我们推导出离线单纯形等角紧框架分类器，其权重作为类中心且实现最大化等距分布。为增强各类特征簇的紧凑性，我们在分类损失中引入自适应类别特定间隔，促使特征向类中心靠拢。在VOC、COCO两个少样本基准数据集及长尾数据集LVIS上的实验表明，单一模型即可在有效提升新类泛化能力的同时，不损害基础类的检测性能。