Semantic Object Accuracy for Generative Text-to-Image Synthesis
This addresses the problem of accurate evaluation for text-to-image models, which is crucial for researchers and developers in generative AI, though it is incremental as it builds on existing object detection and evaluation methods.
The paper tackles the challenge of evaluating text-to-image synthesis models by introducing a new metric called Semantic Object Accuracy (SOA), which measures if generated images contain objects mentioned in captions, and shows that SOA aligns with human rankings better than existing metrics like Inception Score.
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.