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Document-based Visual Question Answering examines the document understanding of document images in conditions of natural language questions.
Antol, S., Agrawal, A., Lu, J., Mitchell, M., Batra, D., Zitnick, C.L., Parikh, D.: Vqa: Visual question answering. In: Proceedings of the IEEE international conference on computer vision. pp. 2425–2433 (2015)
2015
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Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., Parikh, D.: Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6904–6913 (2017)
2017
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Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., Girshick, R.: Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2901–2910 (2017)
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Kembhavi, A., Seo, M., Schwenk, D., Choi, J., Farhadi, A., Hajishirzi, H.: Are you smarter than a sixth grader? textbook question answering for multimodal machine comprehension. In: Proceedings of the IEEE Conference on Computer Vision and Pattern recognition. pp. 4999–5007 (2017)
2017
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Kafle, K., Price, B., Cohen, S., Kanan, C.: Dvqa: Understanding data visualizations via question answering. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 5648–5656 (2018)
2018
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Biten, A.F., Tito, R., Mafla, A., Gomez, L., Rusinol, M., Valveny, E., Jawahar, C., Karatzas, D.: Scene text visual question answering. In: Proceedings of the IEEE/CVF international conference on computer vision. pp. 4291–4301 (2019)
2019
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Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186 (2019)
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Huang, Z., Chen, K., He, J., Bai, X., Karatzas, D., Lu, S., Jawahar, C.: Icdar2019 competition on scanned receipt ocr and information extraction. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1516–1520. IEEE (2019)
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Hudson, D.A., Manning, C.D.: Gqa: A new dataset for real-world visual reasoning and compositional question answering. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 6700–6709 (2019)
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Mishra, A., Shekhar, S., Singh, A.K., Chakraborty, A.: Ocr-vqa: Visual question answering by reading text in images. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 947–952. IEEE (2019)
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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)
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Singh, A., Natarajan, V., Shah, M., Jiang, Y., Chen, X., Batra, D., Parikh, D., Rohrbach, M.: Towards vqa models that can read. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8317–8326 (2019)
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Tan, H., Bansal, M.: Lxmert: Learning cross-modality encoder representations from transformers. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 5100–5111 (2019)
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Mathew, M., Karatzas, D., Jawahar, C.: Docvqa: A dataset for vqa on document images. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 2200–2209 (2021)
2021
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Rausch, J., Martinez, O., Bissig, F., Zhang, C., Feuerriegel, S.: Docparser: Hierarchical document structure parsing from renderings. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 4328–4338 (2021)
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Shen, Z., Zhang, R., Dell, M., Lee, B.C.G., Carlson, J., Li, W.: Layoutparser: A unified toolkit for deep learning based document image analysis. In: Document Analysis and Recognition–ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part I 16. pp. 131–146. Springer (2021)
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Tanaka, R., Nishida, K., Yoshida, S.: Visualmrc: Machine reading comprehension on document images. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 13878–13888 (2021)
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2019
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Chaudhry, R., Shekhar, S., Gupta, U., Maneriker, P., Bansal, P., Joshi, A.: Leaf-qa: Locate, encode & attend for figure question answering. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 3512–3521 (2020)
2020
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Hu, R., Singh, A., Darrell, T., Rohrbach, M.: Iterative answer prediction with pointer-augmented multimodal transformers for textvqa. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9992–10002 (2020)
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Methani, N., Ganguly, P., Khapra, M.M., Kumar, P.: Plotqa: Reasoning over scientific plots. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1527–1536 (2020)
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Wang, X., Liu, Y., Shen, C., Ng, C.C., Luo, C., Jin, L., Chan, C.S., Hengel, A.v.d., Wang, L.: On the general value of evidence, and bilingual scene-text visual question answering. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 10126–10135 (2020)
2020
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Xu, Y., Li, M., Cui, L., Huang, S., Wei, F., Zhou, M.: Layoutlm: Pre-training of text and layout for document image understanding. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. pp. 1192–1200 (2020)
2020
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Davis, B., Morse, B., Price, B., Tensmeyer, C., Wiginton, C.: Visual fudge: Form understanding via dynamic graph editing. In: International Conference on Document Analysis and Recognition. pp. 416–431. Springer (2021)
2021
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Kim, W., Son, B., Kim, I.: Vilt: Vision-and-language transformer without convolution or region supervision. In: International Conference on Machine Learning. pp. 5583–5594. PMLR (2021)
2021
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2021
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Xu, Y., Xu, Y., Lv, T., Cui, L., Wei, F., Wang, G., Lu, Y., Florencio, D., Zhang, C., Che, W., et al.: Layoutlmv2: Multi-modal pre-training for visually-rich document understanding. In: 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). pp. 2579–2591 (2021)
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Zhang, P., Li, C., Qiao, L., Cheng, Z., Pu, S., Niu, Y., Wu, F.: Vsr: a unified framework for document layout analysis combining vision, semantics and relations. In: International Conference on Document Analysis and Recognition. pp. 115–130. Springer (2021)
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Ding, Y., Huang, Z., Wang, R., Zhang, Y., Chen, X., Ma, Y., Chung, H., Han, S.C.: V-doc: Visual questions answers with documents. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21492–21498 (2022)
2022
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Luo, S., Ding, Y., Long, S., Poon, J., Han, S.C.: Doc-gcn: Heterogeneous graph convolutional networks for document layout analysis. In: Proceedings of the 29th International Conference on Computational Linguistics. pp. 2906–2916 (2022)
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Mathew, M., Bagal, V., Tito, R., Karatzas, D., Valveny, E., Jawahar, C.: Infographicvqa. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1697–1706 (2022)
2022
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2022
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