  Image captioning has conventionally relied on reference-based automatic
evaluations, where machine captions are compared against captions written by
humans. This is in stark contrast to the reference-free manner in which humans
assess caption quality.
  In this paper, we report the surprising empirical finding that CLIP (Radford
et al., 2021), a cross-modal model pretrained on 400M image+caption pairs from
the web, can be used for robust automatic evaluation of image captioning
without the need for references. Experiments spanning several corpora
demonstrate that our new reference-free metric, CLIPScore, achieves the highest
correlation with human judgements, outperforming existing reference-based
metrics like CIDEr and SPICE. Information gain experiments demonstrate that
CLIPScore, with its tight focus on image-text compatibility, is complementary
to existing reference-based metrics that emphasize text-text similarities.
Thus, we also present a reference-augmented version, RefCLIPScore, which
achieves even higher correlation. Beyond literal description tasks, several
case studies reveal domains where CLIPScore performs well (clip-art images,
alt-text rating), but also where it is relatively weaker vs reference-based
metrics, e.g., news captions that require richer contextual knowledge.
