  Existing Image Captioning (IC) systems model words as atomic units in
captions and are unable to exploit the structural information in the words.
This makes representation of rare words very difficult and out-of-vocabulary
words impossible. Moreover, to avoid computational complexity, existing IC
models operate over a modest sized vocabulary of frequent words, such that the
identity of rare words is lost. In this work we address this common limitation
of IC systems in dealing with rare words in the corpora. We decompose words
into smaller constituent units 'subwords' and represent captions as a sequence
of subwords instead of words. This helps represent all words in the corpora
using a significantly lower subword vocabulary, leading to better parameter
learning. Using subword language modeling, our captioning system improves
various metric scores, with a training vocabulary size approximately 90% less
than the baseline and various state-of-the-art word-level models. Our
quantitative and qualitative results and analysis signify the efficacy of our
proposed approach.
