PDF-WuKong: A Large Multimodal Model for Efficient Long PDF Reading with End-to-End Sparse Sampling

Huang, Z., Chen, K., He, J., Bai, X., Karatzas, D., Lu, S., Jawahar, C.: Icdar2019 competition on scanned receipt ocr and information extraction. In: ICDAR. pp. 1516–1520 (2019)

2019

Earlier work this paper cites.

Mishra, A., Shekhar, S., Singh, A.K., Chakraborty, A.: Ocr-vqa: Visual question answering by reading text in images. In: ICDAR. pp. 947–952 (2019)

2019

Earlier work this paper cites.

Park, S., Shin, S., Lee, B., Lee, J., Surh, J., Seo, M., Lee, H.: Cord: A consolidated receipt dataset for post-ocr parsing. In: Workshop on Document Intelligence at NeurIPS (2019)

2019

Earlier work this paper cites.

Zhong, X., Tang, J., Yepes, A.J.: Publaynet: largest dataset ever for document layout analysis. In: 2019 International conference on document analysis and recognition (ICDAR). pp. 1015–1022. IEEE (2019)

2019

Earlier work this paper cites.

Beltagy, I., Peters, M.E., Cohan, A.: Longformer: The long-document transformer. arXiv preprint arXiv:2004.05150 (2020)

Original

2020

Earlier work this paper cites.

Li, M., Xu, Y., Cui, L., Huang, S., Wei, F., Li, Z., Zhou, M.: Docbank: A benchmark dataset for document layout analysis. arXiv preprint arXiv:2006.01038 (2020)

Original

2020

Earlier work this paper cites.

Zaheer, M., Guruganesh, G., Dubey, K.A., Ainslie, J., Alberti, C., Ontanon, S., Pham, P., Ravula, A., Wang, Q., Yang, L., et al.: Big bird: Transformers for longer sequences. Advances in neural information processing systems 33

2020

Earlier work this paper cites.

Dasigi, P., Lo, K., Beltagy, I., Cohan, A., Smith, N.A., Gardner, M.: A dataset of information-seeking questions and answers anchored in research papers. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 4599–4610 (2021)

2021

Earlier work this paper cites.

Huang, L., Cao, S., Parulian, N., Ji, H., Wang, L.: Efficient attentions for long document summarization. In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. pp. 1419–1436 (2021)

2021

Earlier work this paper cites.

Mathew, M., Karatzas, D., Jawahar, C.V.: Docvqa: A dataset for vqa on document images. In: WACV. pp. 2200–2209 (2021)

2021

Earlier work this paper cites.

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)

2022

Earlier work this paper cites.

Masry, A., Long, D.X., Tan, J.Q., Joty, S., Hoque, E.: Chartqa: A benchmark for question answering about charts with visual and logical reasoning. arXiv preprint arXiv:2203.10244 (2022)

Original

2022

Earlier work this paper cites.

Mathew, M., Bagal, V., Tito, R., Karatzas, D., Valveny, E., Jawahar, C.: Infographicvqa. In: WACV. pp. 1697–1706 (2022)

2022

Earlier work this paper cites.

Pfitzmann, B., Auer, C., Dolfi, M., Nassar, A.S., Staar, P.: Doclaynet: a large human-annotated dataset for document-layout segmentation. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. pp. 3743–3751 (2022)

2022

Earlier work this paper cites.

Bai, J., Bai, S., Yang, S., Wang, S., Tan, S., Wang, P., Lin, J., Zhou, C., Zhou, J.: Qwen-vl: A versatile vision-language model for understanding, localization, text reading, and beyond. arXiv preprint arXiv:2308.12966 (2023)

Original

2023

Earlier work this paper cites.

Feng, H., Liu, Q., Liu, H., Zhou, W., Li, H., Huang, C.: Docpedia: Unleashing the power of large multimodal model in the frequency domain for versatile document understanding. arXiv preprint arXiv:2311.11810 (2023)

Original

2023

Earlier work this paper cites.

Feng, H., Wang, Z., Tang, J., Lu, J., Zhou, W., Li, H., Huang, C.: Unidoc: A universal large multimodal model for simultaneous text detection, recognition, spotting and understanding (2023), https://arxiv.org/abs/2308.11592

Original

2023

Earlier work this paper cites.

OpenAI: Gpt-4v(ision) system card. https://openai.com/contributions/gpt-4v (2023)

2023

Earlier work this paper cites.

Saad-Falcon, J., Barrow, J., Siu, A., Nenkova, A., Yoon, D.S., Rossi, R.A., Dernoncourt, F.: Pdftriage: Question answering over long, structured documents. arXiv preprint arXiv:2309.08872 (2023)

Original

2023

Earlier work this paper cites.

Šimsa, Š., Šulc, M., Uřičář, M., Patel, Y., Hamdi, A., Kocián, M., Skalickỳ, M., Matas, J., Doucet, A., Coustaty, M., et al.: Docile benchmark for document information localization and extraction pp. 147–166 (2023)

2023

Earlier work this paper cites.

Tanaka, R., Nishida, K., Nishida, K., Hasegawa, T., Saito, I., Saito, K.: Slidevqa: A dataset for document visual question answering on multiple images. In: AAAI. pp. 13636–13645 (2023)

2023

Earlier work this paper cites.

Tito, R., Karatzas, D., Valveny, E.: Hierarchical multimodal transformers for multipage docvqa. Pattern Recognition 144

2023

Earlier work this paper cites.

Tworkowski, S., Staniszewski, K., Pacek, M.a., Wu, Y., Michalewski, H., Mił oś, P.: Focused transformer: Contrastive training for context scaling. In: Advances in Neural Information Processing Systems. vol. 36, pp. 42661–42688 (2023)

2023

Earlier work this paper cites.

Van Landeghem, J., Tito, R., Borchmann, Ł., Pietruszka, M., Joziak, P., Powalski, R., Jurkiewicz, D., Coustaty, M., Anckaert, B., Valveny, E., et al.: Document understanding dataset and evaluation (dude). In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19528–19540 (2023)

2023

Earlier work this paper cites.

Ye, J., Hu, A., Xu, H., Ye, Q., Yan, M., Dan, Y., Zhao, C., Xu, G., Li, C., Tian, J., et al.: mplug-docowl: Modularized multimodal large language model for document understanding. arXiv preprint arXiv:2307.02499 (2023)

Original

2023

Earlier work this paper cites.

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. arXiv preprint arXiv:2310.05126 (2023)

Original

2023

Earlier work this paper cites.

Yue, S., Chen, W., Wang, S., Li, B., Shen, C., Liu, S., Zhou, Y., Xiao, Y., Yun, S., Huang, X., et al.: Disc-lawllm: Fine-tuning large language models for intelligent legal services. arXiv preprint arXiv:2309.11325 (2023)

Original

2023

Earlier work this paper cites.

Grobid. https://github.com/kermitt2/grobid (2008–2024)

2024

Cited alongside, same era.

Appalaraju, S., Tang, P., Dong, Q., Sankaran, N., Zhou, Y., Manmatha, R.: Docformerv2: Local features for document understanding. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 38, pp. 709–718 (2024)

2024

Cited alongside, same era.

Blau, T., Fogel, S., Ronen, R., Golts, A., Ganz, R., Ben Avraham, E., Aberdam, A., Tsiper, S., Litman, R.: Gram: Global reasoning for multi-page vqa. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15598–15607 (2024)

2024

Cited alongside, same era.

Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., Liu, Z.: Bge m3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv preprint arXiv:2402.03216 (2024)

Original

2024

Cited alongside, same era.

Wang, W., Zhang, S., Ren, Y., Duan, Y., Li, T., Liu, S., Hu, M., Chen, Z., Zhang, K., Lu, L., et al.: Needle in a multimodal haystack. arXiv preprint arXiv:2406.07230 (2024)

Original

2024

Closest in time.

Wei, H., Kong, L., Chen, J., Zhao, L., Ge, Z., Yang, J., Sun, J., Han, C., Zhang, X.: Vary: Scaling up the vision vocabulary for large vision-language model. In: European Conference on Computer Vision. pp. 408–424. Springer (2024)

2024

Closest in time.

Wu, W., Cai, Y., Shen, C., Zhang, D., Fu, Y., Zhou, H., Luo, P.: End-to-end video text spotting with transformer. International Journal of Computer Vision 132

2024

Closest in time.

Xia, R., Mao, S., Yan, X., Zhou, H., Zhang, B., Peng, H., Pi, J., Fu, D., Wu, W., Ye, H., et al.: Docgenome: An open large-scale scientific document benchmark for training and testing multi-modal large language models. arXiv preprint arXiv:2406.11633 (2024)

Original

2024

Closest in time.