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In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language models.
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2021
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Huang, Y., Lv, T., Cui, L., Lu, Y., Wei, F.: Layoutlmv3: Pre-training for document ai with unified text and image masking. In: Proceedings of the 30th ACM International Conference on Multimedia. pp. 4083–4091 (2022)
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Lee, K., Joshi, M., Turc, I.R., Hu, H., Liu, F., Eisenschlos, J.M., Khandelwal, U., Shaw, P., Chang, M.W., Toutanova, K.: Pix2struct: Screenshot parsing as pretraining for visual language understanding. In: International Conference on Machine Learning. pp. 18893–18912. PMLR (2023)
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Wang, D., Raman, N., Sibue, M., Ma, Z., Babkin, P., Kaur, S., Pei, Y., Nourbakhsh, A., Liu, X.: Docllm: A layout-aware generative language model for multimodal document understanding (2023)
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2023
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2023
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Ye, J., Hu, A., Xu, H., Ye, Q., Yan, M., Xu, G., Li, C., Tian, J., Qian, Q., Zhang, J., et al.: Ureader: Universal ocr-free visually-situated language understanding with multimodal large language model. In: The 2023 Conference on Empirical Methods in Natural Language Processing (2023)
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2023
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Appalaraju, S., Tang, P., Dong, Q., Sankaran, N., Zhou, Y., Manmatha, R.: Docformerv2: Local features for document understanding. In: AAAI Conference on Artificial Intelligence (2024)
2024
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2024
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2024
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Tanaka, R., Iki, T., Nishida, K., Saito, K., Suzuki, J.: Instructdoc: A dataset for zero-shot generalization of visual document understanding with instructions (2024)
2024
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