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license: odc-by |
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--- |
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#### Model for the paper: [Harnessing Webpage Uis For Text Rich Visual Understanding](https://arxiv.org/abs/2410.13824) |
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🌐 [Homepage](https://neulab.github.io/MultiUI/) | 🐍 [GitHub](https://github.com/neulab/multiui) | 📖 [arXiv](https://arxiv.org/abs/2410.13824) |
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## Introduction |
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We introduce **MultiUI**, a dataset containing 7.3 million samples from 1 million websites, covering diverse multi- modal tasks and UI layouts. Models trained on **MultiUI** not only excel in web UI tasks—achieving up to a 48% improvement on VisualWebBench and a 19.1% boost in action accuracy on a web agent dataset Mind2Web—but also generalize surprisingly well to non-web UI tasks and even to non-UI domains, such as document understanding, OCR, and chart interpretation. |
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<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/vk7yT4Y7ydBOHM6BojmlI.mp4"></video> |
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## Model Performance |
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## Contact |
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* Junpeng Liu: [email protected] |
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* Xiang Yue: [email protected] |
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## Citation |
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If you find this work helpful, please cite out paper: |
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```` |
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@misc{liu2024harnessingwebpageuistextrich, |
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title={Harnessing Webpage UIs for Text-Rich Visual Understanding}, |
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author={Junpeng Liu and Tianyue Ou and Yifan Song and Yuxiao Qu and Wai Lam and Chenyan Xiong and Wenhu Chen and Graham Neubig and Xiang Yue}, |
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year={2024}, |
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eprint={2410.13824}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CV}, |
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url={https://arxiv.org/abs/2410.13824}, |
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} |
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```` |
