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

TL;DR

This paper introduces a simple, effective method for generating large annotated datasets for instance detection by cutting and pasting objects onto backgrounds, reducing data collection effort and improving model performance.

Contribution

The authors propose a novel data synthesis technique that ensures patch-level realism, enabling effective training of object detectors with minimal real data and outperforming existing methods.

Findings

Synthetic data with patch-level realism improves detection performance.

Combining synthetic and small amounts of real data surpasses full real data training.

Method outperforms existing synthesis approaches on benchmark datasets.

Abstract

A major impediment in rapidly deploying object detection models for instance detection is the lack of large annotated datasets. For example, finding a large labeled dataset containing instances in a particular kitchen is unlikely. Each new environment with new instances requires expensive data collection and annotation. In this paper, we propose a simple approach to generate large annotated instance datasets with minimal effort. Our key insight is that ensuring only patch-level realism provides enough training signal for current object detector models. We automatically `cut' object instances and `paste' them on random backgrounds. A naive way to do this results in pixel artifacts which result in poor performance for trained models. We show how to make detectors ignore these artifacts during training and generate data that gives competitive performance on real data. Our method outperforms existing synthesis approaches and when combined with real images improves relative performance by more than 21% on benchmark datasets. In a cross-domain setting, our synthetic data combined with just 10% real data outperforms models trained on all real data.