  In this study, we introduce a low cost method for generating descriptions
from images containing novel objects. Generally, constructing a model, which
can explain images with novel objects, is costly because of the following: (1)
collecting a large amount of data for each category, and (2) retraining the
entire system. If humans see a small number of novel objects, they are able to
estimate their properties by associating their appearance with known objects.
Accordingly, we propose a method that can explain images with novel objects
without retraining using the word embeddings of the objects estimated from only
a small number of image features of the objects. The method can be integrated
with general image-captioning models. The experimental results show the
effectiveness of our approach.
