Text-to-image synthesis has made encouraging progress and attracted lots of
public attention recently. However, popular evaluation metrics in this area,
like the Inception Score and Fr'echet Inception Distance, incur several issues.
First of all, they cannot explicitly assess the perceptual quality of generated
images and poorly reflect the semantic alignment of each text-image pair. Also,
they are inefficient and need to sample thousands of images to stabilise their
evaluation results. In this paper, we propose to evaluate text-to-image
generation performance by directly estimating the likelihood of the generated
images using a pre-trained likelihood-based text-to-image generative model,
i.e., a higher likelihood indicates better perceptual quality and better
text-image alignment. To prevent the likelihood of being dominated by the
non-crucial part of the generated image, we propose several new designs to
develop a credit assignment strategy based on the semantic and perceptual
significance of the image patches. In the experiments, we evaluate the proposed
metric on multiple popular text-to-image generation models and datasets in
accessing both the perceptual quality and the text-image alignment. Moreover,
it can successfully assess the generation ability of these models with as few
as a hundred samples, making it very efficient in practice.

我们提出了一种基于似然的文本到图像生成模型，通过直接估计生成图像的似然来评估其生成性能，并通过语义和感知显著性的图像补丁的信用分配策略改进了评估指标。该方法在感知质量和文本 - 图像对齐方面能够高效地评估多种流行的文本到图像生成模型和数据集的生成能力。