Object detection has achieved promising success, but requires large-scale fully-annotated data, which is time-consuming and labor-extensive. Therefore, we consider the problem of mixed supervised object detection, which learns novel object categories using weak annotations with the help of full annotations of existing base object categories. Previous works on mixed supervised object detection mainly learn the class-agnostic objectness from fully-annotated categories, which can be transferred to…