class-aware与class-agnostic的含义，区别，应用场景
先给出官方的原文解释：
class-aware
For a class-aware detector, if you feed it an image, it will return a set of bounding boxes, each box associated with the class of the object inside (i.e. dog, cat, car). It means that by the time the detector finished detecting, it knows what type of object was detected.
class-agnostic
For class-agnostic detector, it detects a bunch of objects without knowing what class they belong to. To put it simply, they only detect “foreground” objects. Foreground is a broad term, but usually it is a set that contains all specific classes we want to find in an image, i.e. foreground = {cat, dog, car, airplane, …}. Since it doesn’t know the class of the object it detected, we call it class-agnostic.
个人理解：
class-aware的输入是一张图片，返回的是对应需要检测的每一个类的bbox,即检测器在完成检测的同时也知道所检测的每一个bbox属于哪一类
class-agnostic的输入是一张图片，返回的是一组对象，但是并不知道这组对象中每一个具体属于哪一类，即只能进行前景与背景的检测
class-agnostic检测器通常用来进行RPN，在该检测器之后还需要使用一个专门的分类器进行每个类别的检测