One of the challenges of supervised learning training is the need to procure substantial
amount of tagged data. A well-known method of solving this problem is to use synthetic data
in a copy-paste fashion so that we cut objects and paste them onto relevant backgrounds.
Pasting the objects naively results in artifacts that cause models to give poor results on real
data. We present a new method for pasting objects on different backgrounds so that the
dataset created gives competitive performance on real data by only treating the border of the …

One of the challenges of supervised learning training is the need to procure an substantial
amount of tagged data. A well-known method of solving this problem is to use synthetic data
in a copy-paste fashion, so that we cut objects and paste them onto relevant backgrounds.
Pasting the objects naively results in artifacts that cause models to give poor results on real
data. We present a new method for cleanly pasting objects on different backgrounds so that
the dataset created gives competitive performance on real data. The main emphasis is on …