论文标题
基于注意力的几个射击对象检测的统一框架
A Unified Framework for Attention-Based Few-Shot Object Detection
论文作者
论文摘要
在计算机视觉中，很少有射击对象检测（FSOD）是一个快速增长的领域。它包括找到一组给定类别的所有出现，每个类别只有几个注释的示例。已经提出了许多方法来应对这一挑战，其中大多数是基于注意机制的。但是，各种各样的经典对象检测框架和训练策略使方法之间的性能比较很困难。特别是，对于基于注意力的FSOD方法，比较不同注意机制对性能的影响很费力。本文旨在填补这一缺点。为此，提出了一个灵活的框架，以实施文献中可用的大多数注意力技术。为了正确引入这样的框架，首先提供了现有FSOD方法的详细审查。然后将一些不同的注意机制在框架内重新成熟，并与固定的所有其他参数进行比较。
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.
