  Generic object detection is one of the most fundamental problems in computer
vision, yet it is difficult to provide all the bounding-box-level annotations
aiming at large-scale object detection for thousands of categories. In this
paper, we present a novel cross-supervised learning pipeline for large-scale
object detection, denoted as CS-R-FCN. First, we propose to utilize the data
flow of image-level annotated images in the fully-supervised two-stage object
detection framework, leading to cross-supervised learning combining
bounding-box-level annotated data and image-level annotated data. Second, we
introduce a semantic aggregation strategy utilizing the relationships among the
cross-supervised categories to reduce the unreasonable mutual inhibition
effects during the feature learning. Experimental results show that the
proposed CS-R-FCN improves the mAP by a large margin compared to previous
related works.
