Few-Shot Object Detection (FSOD) is a rapidly growing field in computer vision. It consists in finding all occurrences of a given set of classes with only a few annotated examples for each class. Numerous methods have been proposed to address this challenge and most of them are based on attention mechanisms. However, the great variety of classic object detection frameworks and training strategies makes performance comparison between methods difficult. In particular, for attention-based FSOD methods, it is laborious to compare the impact of the different attention mechanisms on performance. This paper aims at filling this shortcoming. To do so, a flexible framework is proposed to allow the implementation of most of the attention techniques available in the literature. To properly introduce such a framework, a detailed review of the existing FSOD methods is firstly provided. Some different attention mechanisms are then reimplemented within the framework and compared with all other parameters fixed.
翻译:很少热对象探测(FSOD)是计算机视野中一个迅速增长的领域,它包括寻找每类中只有几个附加说明的例子的一组特定班级的所有情况,提出了许多方法来应对这一挑战,其中多数基于注意机制。然而,由于典型的物体探测框架和培训战略多种多样,难以对方法进行性能比较。特别是,对于以注意为基础的FSOD方法,比较不同注意机制对业绩的影响是很困难的。本文件旨在弥补这一缺陷。为此,提议了一个灵活的框架,以便实施文献中现有的大多数注意技术。为了适当采用这样一个框架,首先对现有的FSOD方法进行详细审查,然后在框架内重新实施一些不同的注意机制,并与所有其他固定参数进行比较。