Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866917724092891136 |
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| author | Gordon, Brian Bitton, Yonatan Shafir, Yonatan Garg, Roopal Chen, Xi Lischinski, Dani Cohen-Or, Daniel Szpektor, Idan |
| author_facet | Gordon, Brian Bitton, Yonatan Shafir, Yonatan Garg, Roopal Chen, Xi Lischinski, Dani Cohen-Or, Daniel Szpektor, Idan |
| contents | While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment. In this paper, we present a method to provide detailed textual and visual explanation of detected misalignments between text-image pairs. We leverage large language models and visual grounding models to automatically construct a training set that holds plausible misaligned captions for a given image and corresponding textual explanations and visual indicators. We also publish a new human curated test set comprising ground-truth textual and visual misalignment annotations. Empirical results show that fine-tuning vision language models on our training set enables them to articulate misalignments and visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks. Our method code and human curated test set are available at: https://mismatch-quest.github.io/ |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_03766 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment Gordon, Brian Bitton, Yonatan Shafir, Yonatan Garg, Roopal Chen, Xi Lischinski, Dani Cohen-Or, Daniel Szpektor, Idan Computation and Language Computer Vision and Pattern Recognition While existing image-text alignment models reach high quality binary assessments, they fall short of pinpointing the exact source of misalignment. In this paper, we present a method to provide detailed textual and visual explanation of detected misalignments between text-image pairs. We leverage large language models and visual grounding models to automatically construct a training set that holds plausible misaligned captions for a given image and corresponding textual explanations and visual indicators. We also publish a new human curated test set comprising ground-truth textual and visual misalignment annotations. Empirical results show that fine-tuning vision language models on our training set enables them to articulate misalignments and visually indicate them within images, outperforming strong baselines both on the binary alignment classification and the explanation generation tasks. Our method code and human curated test set are available at: https://mismatch-quest.github.io/ |
| title | Mismatch Quest: Visual and Textual Feedback for Image-Text Misalignment |
| topic | Computation and Language Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2312.03766 |