Semantic Object Accuracy for Generative Text-to-Image Synthesis

TL;DR

This paper introduces Semantic Object Accuracy, a new evaluation metric for text-to-image models that measures how well generated images contain objects mentioned in captions, and proposes a model that explicitly models individual objects for improved generation.

Contribution

The paper presents a novel object-aware evaluation metric and a generative model that explicitly models objects, improving the assessment and quality of text-to-image synthesis.

Findings

SOA correlates well with human judgment

Object-aware models outperform global models

SOA provides more meaningful evaluation than Inception Score

Abstract

Generative adversarial networks conditioned on textual image descriptions are capable of generating realistic-looking images. However, current methods still struggle to generate images based on complex image captions from a heterogeneous domain. Furthermore, quantitatively evaluating these text-to-image models is challenging, as most evaluation metrics only judge image quality but not the conformity between the image and its caption. To address these challenges we introduce a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption. The SOA uses a pre-trained object detector to evaluate if a generated image contains objects that are mentioned in the image caption, e.g. whether an image generated from "a car driving down the street" contains a car. We perform a user study comparing several text-to-image models and show that our SOA metric ranks the models the same way as humans, whereas other metrics such as the Inception Score do not. Our evaluation also shows that models which explicitly model objects outperform models which only model global image characteristics.