  We present a conceptually simple, flexible and general framework for
cross-dataset training in object detection. Given two or more already labeled
datasets that target for different object classes, cross-dataset training aims
to detect the union of the different classes, so that we do not have to label
all the classes for all the datasets. By cross-dataset training, existing
datasets can be utilized to detect the merged object classes with a single
model. Further more, in industrial applications, the object classes usually
increase on demand. So when adding new classes, it is quite time-consuming if
we label the new classes on all the existing datasets. While using
cross-dataset training, we only need to label the new classes on the new
dataset. We experiment on PASCAL VOC, COCO, WIDER FACE and WIDER Pedestrian
with both solo and cross-dataset settings. Results show that our cross-dataset
pipeline can achieve similar impressive performance simultaneously on these
datasets compared with training independently.
