  Text-to-image generation and image captioning are recently emerged as a new
experimental paradigm to assess machine intelligence. They predict continuous
quantity accompanied by their sampling techniques in the generation, making
evaluation complicated and intractable to get marginal distributions. Based on
a recent trend that multimodal generative evaluations exploit a
vison-and-language pre-trained model, we propose the negative Gaussian
cross-mutual information using the CLIP features as a unified metric, coined by
Mutual Information Divergence (MID). To validate, we extensively compare it
with competing metrics using carefully-generated or human-annotated judgments
in text-to-image generation and image captioning tasks. The proposed MID
significantly outperforms the competitive methods by having consistency across
benchmarks, sample parsimony, and robustness toward the exploited CLIP model.
We look forward to seeing the underrepresented implications of the Gaussian
cross-mutual information in multimodal representation learning and the future
works based on this novel proposition.
