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Open Graph

title

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Are Multimodal Models Robust to Image and Text Perturbations?

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site name

author

Jielin Qiu, Yi Zhu, Xingjian Shi, Florian Wenzel, Zhiqiang Tang, Ding Zhao, Bo Li, Mu Li

updated

2026-02-17 08:35:33

raw text

Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift Benchmarking Robustness of Multimodal Image-Text Models under Distribution Shift Jielin Qiu 1 , Yi Zhu 2 , Xingjian Shi 2 , Florian Wenzel 3 , Zhiqiang Tang 4 , Ding Zhao 1 , Bo Li 4,5 , Mu Li 2 1 Carnegie Mellon University, 2 Boson AI, 3 Mirelo AI 4 Amazon Web Services, 5 University of Chicago Journal of Data-centric Machine Learning Research (DMLR) 2024 Paper Code Data Multimodal models are sensitive to image/text perturbations (original image-text pairs are shown in blue boxes, perturbed ones are in red). Image captioning (Top): Adding image perturbations can result in incorrect captions, e.g., the tabby kitten is mistakenly described as a woman/dog. Text-to-image generation (bottom): Applying text perturbations can result in the generated images containing incomplete visual information, e.g., the tree is missing i...

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