Abstract
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.
Abstract (translated)
通用 few-shot 物体检测的目标是在拥有大量标注的基类和仅有少量训练数据的新类上都实现准确的检测。现有的方法通过牺牲基类性能来增强 few-shot 泛化,或者在基类检测精度不变的情况下,仅少量改善新类适应度。在本文中,我们指出原因是对所有类没有足够的分类特征学习。因此,我们提出了一个新的训练框架 DiGeo,以学习类别间分离和类别内密集性的几何aware特征。为了指导特征簇的分离,我们推导了一个 offline 在线的等角紧框(ETF)分类器,其权重作为类中心,被最大化且同样地分离每个类。为了收紧每个类的特征簇,我们将其自适应类特异性margins 添加到分类损失中,并鼓励接近类中心的特征。在两个 few-shot 基准数据集(VOC 和 COCO)以及一个长尾巴数据集(LVIS)的实验研究中,证明了通过单个模型,我们的方法可以有效地改善新类对基类的泛化,而不会伤害基类检测精度。
URL
https://arxiv.org/abs/2303.09674
