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A major impediment in rapidly deploying object detection models for instancedetection is the lack of large annotated datasets. For example, finding a largelabeled dataset containing instances in a particular kitchen is unlikely. Eachnew environment with new instances requires expensive data collection andannotation. In this paper, we propose a simple approach to generate largeannotated instance datasets with minimal effort. Our key insight is thatensuring only patch-level realism provides enough training signal for currentobject detector models. We automatically `cut' object instances and `paste'them on random backgrounds. A naive way to do this results in pixel artifactswhich result in poor performance for trained models. We show how to makedetectors ignore these artifacts during training and generate data that givescompetitive performance on real data. Our method outperforms existing synthesisapproaches and when combined with real images improves relative performance bymore than 21% on benchmark datasets. In a cross-domain setting, our syntheticdata combined with just 10% real data outperforms models trained on all realdata.

Cut, Paste and Learn: Surprisingly Easy Synthesis for Instance Detection