  Despite considerable progress, state of the art image captioning models
produce generic captions, leaving out important image details. Furthermore,
these systems may even misrepresent the image in order to produce a simpler
caption consisting of common concepts. In this paper, we first analyze both
modern captioning systems and evaluation metrics through empirical experiments
to quantify these phenomena. We find that modern captioning systems return
higher likelihoods for incorrect distractor sentences compared to ground truth
captions, and that evaluation metrics like SPICE can be 'topped' using simple
captioning systems relying on object detectors. Inspired by these observations,
we design a new metric (SPICE-U) by introducing a notion of uniqueness over the
concepts generated in a caption. We show that SPICE-U is better correlated with
human judgements compared to SPICE, and effectively captures notions of
diversity and descriptiveness. Finally, we also demonstrate a general technique
to improve any existing captioning model -- by using mutual information as a
re-ranking objective during decoding. Empirically, this results in more unique
and informative captions, and improves three different state-of-the-art models
on SPICE-U as well as average score over existing metrics.
