@inproceedings{ye-etal-2023-ureader,
title = "{UR}eader: Universal {OCR}-free Visually-situated Language Understanding with Multimodal Large Language Model",
author = "Ye, Jiabo and
Hu, Anwen and
Xu, Haiyang and
Ye, Qinghao and
Yan, Ming and
Xu, Guohai and
Li, Chenliang and
Tian, Junfeng and
Qian, Qi and
Zhang, Ji and
Jin, Qin and
He, Liang and
Lin, Xin and
Huang, Fei",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.187",
doi = "10.18653/v1/2023.findings-emnlp.187",
pages = "2841--2858",
abstract = "Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs. In this work, we propose UReader, a first exploration of universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM). By leveraging the shallow text recognition ability of the MLLM, we only finetuned 1.2{\%} parameters and the training cost is much lower than previous work following domain-specific pretraining and finetuning paradigms. Concretely, UReader is jointly finetuned on a wide range of Visually-situated Language Understanding tasks via a unified instruction format. To enhance the visual text and semantic understanding, we further apply two auxiliary tasks with the same format, namely text reading and key points generation tasks. We design a shape-adaptive cropping module before the encoder-decoder architecture of MLLM to leverage the frozen low-resolution vision encoder for processing high-resolution images. Without downstream finetuning, our single model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks, across 5 domains: documents, tables, charts, natural images, and webpage screenshots. Codes and instruction-tuning datasets will be released.",
}
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<abstract>Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs. In this work, we propose UReader, a first exploration of universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM). By leveraging the shallow text recognition ability of the MLLM, we only finetuned 1.2% parameters and the training cost is much lower than previous work following domain-specific pretraining and finetuning paradigms. Concretely, UReader is jointly finetuned on a wide range of Visually-situated Language Understanding tasks via a unified instruction format. To enhance the visual text and semantic understanding, we further apply two auxiliary tasks with the same format, namely text reading and key points generation tasks. We design a shape-adaptive cropping module before the encoder-decoder architecture of MLLM to leverage the frozen low-resolution vision encoder for processing high-resolution images. Without downstream finetuning, our single model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks, across 5 domains: documents, tables, charts, natural images, and webpage screenshots. Codes and instruction-tuning datasets will be released.</abstract>
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%0 Conference Proceedings
%T UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model
%A Ye, Jiabo
%A Hu, Anwen
%A Xu, Haiyang
%A Ye, Qinghao
%A Yan, Ming
%A Xu, Guohai
%A Li, Chenliang
%A Tian, Junfeng
%A Qian, Qi
%A Zhang, Ji
%A Jin, Qin
%A He, Liang
%A Lin, Xin
%A Huang, Fei
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ye-etal-2023-ureader
%X Text is ubiquitous in our visual world, conveying crucial information, such as in documents, websites, and everyday photographs. In this work, we propose UReader, a first exploration of universal OCR-free visually-situated language understanding based on the Multimodal Large Language Model (MLLM). By leveraging the shallow text recognition ability of the MLLM, we only finetuned 1.2% parameters and the training cost is much lower than previous work following domain-specific pretraining and finetuning paradigms. Concretely, UReader is jointly finetuned on a wide range of Visually-situated Language Understanding tasks via a unified instruction format. To enhance the visual text and semantic understanding, we further apply two auxiliary tasks with the same format, namely text reading and key points generation tasks. We design a shape-adaptive cropping module before the encoder-decoder architecture of MLLM to leverage the frozen low-resolution vision encoder for processing high-resolution images. Without downstream finetuning, our single model achieves state-of-the-art ocr-free performance in 8 out of 10 visually-situated language understanding tasks, across 5 domains: documents, tables, charts, natural images, and webpage screenshots. Codes and instruction-tuning datasets will be released.
%R 10.18653/v1/2023.findings-emnlp.187
%U https://aclanthology.org/2023.findings-emnlp.187
%U https://doi.org/10.18653/v1/2023.findings-emnlp.187
%P 2841-2858
Markdown (Informal)
[UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model](https://aclanthology.org/2023.findings-emnlp.187) (Ye et al., Findings 2023)
ACL
- Jiabo Ye, Anwen Hu, Haiyang Xu, Qinghao Ye, Ming Yan, Guohai Xu, Chenliang Li, Junfeng Tian, Qi Qian, Ji Zhang, Qin Jin, Liang He, Xin Lin, and Fei Huang. 2023. UReader: Universal OCR-free Visually-situated Language Understanding with Multimodal Large Language Model. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 2841–2858, Singapore. Association for Computational Linguistics.