Our proposal is similar to CIDEr in that we capture consensus information given a set of human references.
we significantly differ in that (i) we use the references to explicitly model object importance, instead of directly comparing the candidates against the references; (ii) we perform word matching in a semantic space using word embeddings rather than surface forms.