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Text-to-image synthesis has made encouraging progress and attracted lots ofpublic 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 generatedimages and poorly reflect the semantic alignment of each text-image pair. Also,they are inefficient and need to sample thousands of images to stabilise theirevaluation results. In this paper, we propose to evaluate text-to-imagegeneration performance by directly estimating the likelihood of the generatedimages using a pre-trained likelihood-based text-to-image generative model,i.e., a higher likelihood indicates better perceptual quality and bettertext-image alignment. To prevent the likelihood of being dominated by thenon-crucial part of the generated image, we propose several new designs todevelop a credit assignment strategy based on the semantic and perceptualsignificance of the image patches. In the experiments, we evaluate the proposedmetric on multiple popular text-to-image generation models and datasets inaccessing both the perceptual quality and the text-image alignment. Moreover,it can successfully assess the generation ability of these models with as fewas a hundred samples, making it very efficient in practice.

Likelihood-Based Text-to-Image Evaluation with Patch-Level Perceptual and Semantic Credit Assignment