What You See is What You Read? Improving Text-Image Alignment Evaluation

• URL: http://arxiv.org/abs/2305.10400v4
• Date: Tue, 26 Dec 2023 15:58:24 GMT
• Title: What You See is What You Read? Improving Text-Image Alignment Evaluation
• Authors: Michal Yarom, Yonatan Bitton, Soravit Changpinyo, Roee Aharoni, Jonathan Herzig, Oran Lang, Eran Ofek, Idan Szpektor
• Abstract summary: We study methods for automatic text-image alignment evaluation. We first introduce SeeTRUE, spanning multiple datasets from both text-to-image and image-to-text generation tasks. We 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.
• Score: 28.722369586165108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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