TableVQA-Bench: A Visual Question Answering Benchmark on Multiple Table Domains

Pasupat, P., Liang, P.: Compositional semantic parsing on semi-structured tables. In: Zong, C., Strube, M. (eds.) Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 1470–1480. Association for Computational Linguistics, Beijing, China (Jul 2015). https://doi.org/10.3115/v1/P15-1142, https://aclanthology.org/P15-1142

2015

Earlier work this paper cites.

Chen, W., Wang, H., Chen, J., Zhang, Y., Wang, H., Li, S., Zhou, X., Wang, W.Y.: Tabfact: A large-scale dataset for table-based fact verification. In: International Conference on Learning Representations (2020), https://openreview.net/forum?id=rkeJRhNYDH

2020

Earlier work this paper cites.

Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: data, model, and evaluation. In: European conference on computer vision. pp. 564–580. Springer (2020)

2020

Earlier work this paper cites.

Zhong, X., ShafieiBavani, E., Jimeno Yepes, A.: Image-based table recognition: data, model, and evaluation. In: European conference on computer vision. pp. 564–580. Springer (2020)

2020

Earlier work this paper cites.

Zheng, X., Burdick, D., Popa, L., Zhong, P., Wang, N.X.R.: Global table extractor (gte): A framework for joint table identification and cell structure recognition using visual context. Winter Conference for Applications in Computer Vision (WACV) (2021)

2021

Earlier work this paper cites.

Masry, A., Do, X.L., Tan, J.Q., Joty, S., Hoque, E.: Chartqa: A benchmark for question answering about charts with visual and logical reasoning. In: Findings of the Association for Computational Linguistics: ACL 2022. pp. 2263–2279 (2022)

2022

Earlier work this paper cites.

Nassar, A., Livathinos, N., Lysak, M., Staar, P.: Tableformer: Table structure understanding with transformers. arXiv preprint arXiv:2203.01017 (2022)

Original

2022

Earlier work this paper cites.

Gpt-4v(ision) system card (2023), https://api.semanticscholar.org/CorpusID:263218031

2023

Earlier work this paper cites.

Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J., Altman, S., Anadkat, S., et al.: Gpt-4 technical report. arXiv preprint arXiv:2303.08774 (2023)

Original

2023

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 frontier large vision-language model with versatile abilities. arXiv preprint arXiv:2308.12966 (2023)

Original

2023

Earlier work this paper cites.

Chiang, W.L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An open-source chatbot impressing gpt-4 with 90%* chatgpt quality (March 2023), https://lmsys.org/blog/2023-03-30-vicuna/

2023

Cited alongside, same era.

Dai, W., Li, J., Li, D., Tiong, A., Zhao, J., Wang, W., Li, B., Fung, P., Hoi, S.: Instructblip: Towards general-purpose vision-language models with instruction tuning. arxiv 2023. arXiv preprint arXiv:2305.06500

Original

2023

Cited alongside, same era.

Hong, W., Wang, W., Lv, Q., Xu, J., Yu, W., Ji, J., Wang, Y., Wang, Z., Dong, Y., Ding, M., et al.: Cogagent: A visual language model for gui agents. arXiv preprint arXiv:2312.08914 (2023)

Original

2023

Cited alongside, same era.

Kim, D., Kim, Y., Kim, D., Lim, Y., Kim, G., Kil, T.: Scob: Universal text understanding via character-wise supervised contrastive learning with online text rendering for bridging domain gap. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19562–19573 (2023)

Liu, F., Wang, X., Yao, W., Chen, J., Song, K., Cho, S., Yacoob, Y., Yu, D.: Mmc: Advancing multimodal chart understanding with large-scale instruction tuning. arXiv preprint arXiv:2311.10774 (2023)

Original

2023

Later among the works it cites.

Liu, H., Li, C., Li, Y., Lee, Y.J.: Improved baselines with visual instruction tuning. arXiv preprint arXiv:2310.03744 (2023)

Original

2023

Later among the works it cites.

Liu, H., Li, C., Wu, Q., Lee, Y.J.: Visual instruction tuning. In: NeurIPS (2023)

2023

Later among the works it cites.

Liu, Y., Duan, H., Zhang, Y., Li, B., Zhang, S., Zhao, W., Yuan, Y., Wang, J., He, C., Liu, Z., et al.: Mmbench: Is your multi-modal model an all-around player? arXiv preprint arXiv:2307.06281 (2023)

Original

2023

Later among the works it cites.

Lu, P., Bansal, H., Xia, T., Liu, J., Li, C., Hajishirzi, H., Cheng, H., Chang, K.W., Galley, M., Gao, J.: Mathvista: Evaluating mathematical reasoning of foundation models in visual contexts. arXiv preprint arXiv:2310.02255 (2023)