The DOI matches its Crossref record; 42 of the 42 references it cites were found in a registry or an index.
DocVQA: A Dataset for VQA on Document Images
Open accessAbstract
We present a new dataset for Visual Question Answering (VQA) on document images called DocVQA. The dataset consists of 50,000 questions defined on 12,000+ document images. Detailed analysis of the dataset in comparison with similar datasets for VQA and reading comprehension is presented. We report several baseline results by adopting existing VQA and reading comprehension models. Although the existing models perform reasonably well on certain types of questions, there is large performance gap compared to human performance (94.36% accuracy). The models need to improve specifically on questions where understanding structure of the document is crucial. The dataset, code and leaderboard are available at docvqa.org.
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Cited by
Grattafiori, A., Dubey, A., Jauhri, A., Pandey, A., Kadian, A., Al-Dahle, A., Letman, A., Mathur, A., Schelten, A., Vaughan, A., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.21783 · Cited by 505
LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding
Xu, Y., Xu, Y., Lv, T., Cui, L., Wei, F., Wang, G., Lu, Y., Florencio, D., Zhang, C., Che, W., et al. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers) · doi:10.18653/v1/2021.acl-long.201 · Cited by 482
(2022) · doi:10.1007/978-3-031-19815-1_29 · Cited by 469
MVBench: A Comprehensive Multi-modal Video Understanding Benchmark
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Kresevic, S., Giuffrè, M., Ajcevic, M., Accardo, A., Crocè, L. S., & Shung, D. L. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01091-y · Cited by 207
Metadata
- Authors
- Mathew, M., Karatzas, D., & Jawahar, C. V.
- Venue
- 2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
- Publisher
- IEEE
- Published
- 2021-01
- Pages
- 2199-2208
- ISSN
- 2472-6737 (ISSN-L)
- OpenAlex
- W3120043490
- Cited by
- 513
- References
- 42
- FWCI
- 16.37
- Type
- conference paper
- Language
- en
- Open access
- green
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