  Training an object instance detector where only a few training object images
are available is a challenging task. One solution is a cut-and-paste method
that generates a training dataset by cutting object areas out of training
images and pasting them onto other background images. A detector trained on a
dataset generated with a cut-and-paste method suffers from the conventional
domain shift problem, which stems from a discrepancy between the source domain
(generated training dataset) and the target domain (real test dataset). Though
state-of-the-art domain adaptation methods are able to reduce this gap, it is
limited because they do not consider the difference of domain gaps of
foreground and background. In this study, we present that the conventional
domain gap can be divided into two sub-domain gaps for foreground and
background. Then, we show that the original cut-and-paste approach suffers from
a new domain gap problem, an unbalanced domain gaps, because it has two
separate source domains for foreground and background, unlike the conventional
domain shift problem. Then, we introduce an advanced cut-and-paste method to
balance the unbalanced domain gaps by diversifying the foreground with GAN
(generative adversarial network)-generated seed images and simplifying the
background using image processing techniques. Experimental results show that
our method is effective for balancing domain gaps and improving the accuracy of
object instance detection in a cluttered indoor environment using only a few
seed images. Furthermore, we show that balancing domain gaps can improve the
detection accuracy of state-of-the-art domain adaptation methods.
