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In this study, we introduce a low cost method for generating descriptionsfrom images containing novel objects. Generally, constructing a model, whichcan explain images with novel objects, is costly because of the following: (1)collecting a large amount of data for each category, and (2) retraining theentire system. If humans see a small number of novel objects, they are able toestimate their properties by associating their appearance with known objects.Accordingly, we propose a method that can explain images with novel objectswithout retraining using the word embeddings of the objects estimated from onlya small number of image features of the objects. The method can be integratedwith general image-captioning models. The experimental results show theeffectiveness of our approach.

Captioning Images with Novel Objects via Online Vocabulary Expansion