The task of automatically generating sentential descriptions of image content has become increasingly popular in recent years, resulting
in the development of large-scale image description datasets and the proposal of various metrics for evaluating image description
generation systems. However, not much work has been done to analyse and understand both datasets and the metrics. In this paper,
we propose using a leave-one-out cross validation (LOOCV) process as a means to analyse multiply annotated, human-authored image
description datasets and the various evaluation metrics, i.e. evaluating one image description against other human-authored descriptions
of the same image. Such an evaluation process affords various insights into the image description datasets and evaluation metrics,
such as the variations of image descriptions within and across datasets and also what the metrics capture. We compute and analyse
(i) human upper-bound performance; (ii) ranked correlation between metric pairs across datasets; (iii) lower-bound performance by
comparing a set of descriptions describing one image to another sentence not describing that image. Interesting observations are made
about the evaluation metrics and image description datasets, and we conclude that such cross-validation methods are extremely useful
for assessing and gaining insights into image description datasets and evaluation metrics for image descriptions
