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DESCRIPTION META TAG

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2026-02-19 22:50:52

raw text

WYSIWYR What You See is What You Read? Improving Text-Image Alignment Evaluation Michal Yarom* ,  Yonatan Bitton* ,  Soravit "Beer" Changpinyo ,  Roee Aharoni ,  Jonathan Herzig ,  Oran Lang ,  Eran Ofek ,  Idan Szpektor ,  Google Research, The Hebrew University of Jerusalem * Equal Contribution NeurIPS 2023 arXiv Code 🤗 Test Dataset 📄 Train Dataset 🖼 Train Images Focusing on image-text alignment, we introduce SeeTRUE, a comprehensive benchmark, and two effective methods: a zero-shot VQA-based approach and a synthetically-trained, fine-tuned model, both enhancing alignment tasks and text-to-image reordering. 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 ev...

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