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

Towards Unique and Informative Captioning of Images