  Image Captioning is a task that requires models to acquire a multi-modal
understanding of the world and to express this understanding in natural
language text. While the state-of-the-art for this task has rapidly improved in
terms of n-gram metrics, these models tend to output the same generic captions
for similar images. In this work, we address this limitation and train a model
that generates more diverse and specific captions through an unsupervised
training approach that incorporates a learning signal from an Image Retrieval
model. We summarize previous results and improve the state-of-the-art on
caption diversity and novelty. We make our source code publicly available
online.
