  This paper addresses the generation of referring expressions that not only
refer to objects correctly but also let humans find them quickly. As a target
becomes relatively less salient, identifying referred objects itself becomes
more difficult. However, the existing studies regarded all sentences that refer
to objects correctly as equally good, ignoring whether they are easily
understood by humans. If the target is not salient, humans utilize
relationships with the salient contexts around it to help listeners to
comprehend it better. To derive this information from human annotations, our
model is designed to extract information from the target and from the
environment. Moreover, we regard that sentences that are easily understood are
those that are comprehended correctly and quickly by humans. We optimized this
by using the time required to locate the referred objects by humans and their
accuracies. To evaluate our system, we created a new referring expression
dataset whose images were acquired from Grand Theft Auto V (GTA V), limiting
targets to persons. Experimental results show the effectiveness of our
approach. Our code and dataset are available at
https://github.com/mikittt/easy-to-understand-REG.

Computer Vision and Pattern Recognition