What You See is What You Read? Improving Text-Image Alignment Evaluation
Authors: name: Michal Yarom, name: Yonatan Bitton, name: Soravit Changpinyo, name: Roee Aharoni, name: Jonathan Herzig, name: Oran Lang, name: Eran Ofek, name: Idan Szpektor

Automatically determining whether a text and a corresponding image are
semantically aligned is a significant challenge for vision-language models,
with applications in generative text-to-image and image-to-text tasks. In this
work, we study methods for automatic text-image alignment evaluation. We first
introduce SeeTRUE: a comprehensive evaluation set, spanning multiple datasets
from both text-to-image and image-to-text generation tasks, with human
judgements for whether a given text-image pair is semantically aligned. We then
describe two automatic methods to determine alignment: the first involving a
pipeline based on question generation and visual question answering models, and
the second employing an end-to-end classification approach by finetuning
multimodal pretrained models. Both methods surpass prior approaches in various
text-image alignment tasks, with significant improvements in challenging cases
that involve complex composition or unnatural images. Finally, we demonstrate
how our approaches can localize specific misalignments between an image and a
given text, and how they can be used to automatically re-rank candidates in
text-to-image generation.