MLLM-Selector: Necessity and Diversity-driven High-Value Data Selection for Enhanced Visual Instruction Tuning

Marti, U.-V., Bunke, H.: The iam-database: an english sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition, 39–46 (2002) https://doi.org/10.1007/s100320200071

2002

Earlier work this paper cites.

Green, S., Heer, J., Manning, C.D.: The efficacy of human post-editing for language translation. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 439–448 (2013)

2013

Earlier work this paper cites.

Medhat, W., Hassan, A., Korashy, H.: Sentiment analysis algorithms and applications: A survey. Ain Shams engineering journal 5

2014

Earlier work this paper cites.

Dong, D., Wu, H., He, W., Yu, D., Wang, H.: Multi-task learning for multiple language translation. In: 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. 1723–1732 (2015)

2015

Earlier work this paper cites.

Ren, M., Kiros, R., Zemel, R.: Exploring models and data for image question answering. arXiv: Learning,arXiv: Learning (2015)

2015

Earlier work this paper cites.

Pasupat, P., Liang, P.: Compositional semantic parsing on semi-structured tables. arXiv preprint arXiv:1508.00305 (2015)

Original

2015

Earlier work this paper cites.

Kembhavi, A., Salvato, M., Kolve, E., Seo, M., Hajishirzi, H., Farhadi, A.: A diagram is worth a dozen images. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part IV 14, pp. 235–251 (2016). Springer

2016

Earlier work this paper cites.

Zhu, Y., Groth, O., Bernstein, M., Fei-Fei, L.: Visual7w: Grounded question answering in images. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016). https://doi.org/10.1109/cvpr.2016.540 . http://dx.doi.org/10.1109/cvpr.2016.540

2016

Earlier work this paper cites.

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30

2017

Earlier work this paper cites.

Duan, N., Tang, D., Chen, P., Zhou, M.: Question generation for question answering. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp. 866–874 (2017)

2017

Earlier work this paper cites.

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

Earlier work this paper cites.

Kahou, S., Michalski, V., Atkinson, e.: Figureqa: An annotated figure dataset for visual reasoning. arXiv: Computer Vision and Pattern Recognition,arXiv: Computer Vision and Pattern Recognition (2017)

2017

Earlier work this paper cites.

Zhong, V., Xiong, C., Socher, R.: Seq2sql: Generating structured queries from natural language using reinforcement learning. Cornell University - arXiv,Cornell University - arXiv (2017)

2017

Earlier work this paper cites.

Iyyer, M., Yih, W.-t., Chang, M.-W.: Search-based neural structured learning for sequential question answering. In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (2017). https://doi.org/10.18653/v1/p17-1167 . http://dx.doi.org/10.18653/v1/p17-1167

2017

Earlier work this paper cites.

Johnson, J., Hariharan, B., Maaten, L., Fei-Fei, L., Zitnick, C.L., Girshick, R.: Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). https://doi.org/10.1109/cvpr.2017.215 . http://dx.doi.org/10.1109/cvpr.2017.215

2017

Earlier work this paper cites.

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: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017). https://doi.org/10.1109/cvpr.2017.571 . http://dx.doi.org/10.1109/cvpr.2017.571

2017

Earlier work this paper cites.

Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

Original

2018

Earlier work this paper cites.

Choi, E., He, H., Iyyer, M., Yatskar, M., Yih, W.-t., Choi, Y., Liang, P., Zettlemoyer, L.: Quac: Question answering in context. arXiv preprint arXiv:1808.07036 (2018)

Original

2018

Earlier work this paper cites.

Gurari, D., Li, Q., Stangl, A.J., Guo, A., Lin, C., Grauman, K., Luo, J., Bigham, J.P.: Vizwiz grand challenge: Answering visual questions from blind people. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3608–3617 (2018)

2018

Earlier work this paper cites.

Lau, J.J., Gayen, S., Ben Abacha, A., Demner-Fushman, D.: A dataset of clinically generated visual questions and answers about radiology images. Scientific Data (2018) https://doi.org/10.1038/sdata.2018.251

2018

Earlier work this paper cites.

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

Earlier work this paper cites.

Suhr, A., Zhou, S., Zhang, A., Zhang, I., Bai, H., Artzi, Y.: A corpus for reasoning about natural language grounded in photographs. arXiv preprint arXiv:1811.00491 (2018)

Original

2018

Earlier work this paper cites.

Jhamtani, H., Berg-Kirkpatrick, T.: Learning to describe differences between pairs of similar images. arXiv preprint arXiv:1808.10584 (2018)

Original

2018

Earlier work this paper cites.

Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., Sutskever, I., et al

2019

Earlier work this paper cites.

Marino, K., Rastegari, M., Farhadi, A., Mottaghi, R.: Ok-vqa: A visual question answering benchmark requiring external knowledge. In: Proceedings of the IEEE/cvf Conference on Computer Vision and Pattern Recognition, pp. 3195–3204 (2019)

2019

Earlier work this paper cites.

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)

2019

Earlier work this paper cites.

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)

2019

Earlier work this paper cites.

Goyal, Y., Khot, T., Agrawal, A., Summers-Stay, D., Batra, D., Parikh, D.: Making the v in vqa matter: Elevating the role of image understanding in visual question answering. International Journal of Computer Vision, 398–414 (2019) https://doi.org/10.1007/s11263-018-1116-0

2019

Earlier work this paper cites.

Acharya, M., Kafle, K., Kanan, C.: Tallyqa: Answering complex counting questions. Proceedings of the AAAI Conference on Artificial Intelligence, 8076–8084 (2019) https://doi.org/10.1609/aaai.v33i01.33018076

2019

Earlier work this paper cites.

Marino, K., Rastegari, M., Farhadi, A., Mottaghi, R.: Ok-vqa: A visual question answering benchmark requiring external knowledge. Cornell University - arXiv,Cornell University - arXiv (2019)

2019

Earlier work this paper cites.

Singh, A., Natarajan, V., Shah, M., Jiang, Y., Chen, X., Batra, D., Parikh, D., Rohrbach, M.: Towards vqa models that can read. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019). https://doi.org/10.1109/cvpr.2019.00851 . http://dx.doi.org/10.1109/cvpr.2019.00851

2019

Earlier work this paper cites.

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

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: 2019 International Conference on Document Analysis and Recognition (ICDAR) (2019). https://doi.org/10.1109/icdar.2019.00156 . http://dx.doi.org/10.1109/icdar.2019.00156

2019

Earlier work this paper cites.

Methani, N., Ganguly, P., Khapra, M., Kumar, P.: Plotqa: Reasoning over scientific plots. Cornell University - arXiv,Cornell University - arXiv (2019)

2019

Earlier work this paper cites.

Zhang, C., Gao, F., Jia, B., Zhu, Y., Zhu, S.-C.: Raven: A dataset for relational and analogical visual reasoning. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019). https://doi.org/10.1109/cvpr.2019.00546 . http://dx.doi.org/10.1109/cvpr.2019.00546

2019

Earlier work this paper cites.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al

2020

Earlier work this paper cites.

Li, X., Yin, X., Li, C., Zhang, P., Hu, X., Zhang, L., Wang, L., Hu, H., Dong, L., Wei, F., et al

2020

Earlier work this paper cites.

Kiela, D., Hamed, F., Mohan, A., Goswami, V., Singh, A., Ringshia, P., Testuggine, D.: The hateful memes challenge: Detecting hate speech in multimodal memes. Cornell University - arXiv,Cornell University - arXiv (2020)

2020

Earlier work this paper cites.

Pont-Tuset, J., Uijlings, J., Changpinyo, S., Soricut, R., Ferrari, V.: Connecting vision and language with localized narratives. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part V 16, pp. 647–664 (2020). Springer

2020

Earlier work this paper cites.

Sidorov, O., Hu, R., Rohrbach, M., Singh, A.: Textcaps: a dataset for image captioning with reading comprehension. Cornell University - arXiv,Cornell University - arXiv (2020)

2020

Earlier work this paper cites.

Obeid, J., Hoque, E.: Chart-to-text: Generating natural language descriptions for charts by adapting the transformer model. arXiv preprint arXiv:2010.09142 (2020)

Original

2020

Earlier work this paper cites.

Chen, M., Tworek, J., Jun, H., Yuan, Q., Pinto, H.P.D.O., Kaplan, J., Edwards, H., Burda, Y., Joseph, N., Brockman, G., et al.: Evaluating large language models trained on code. arXiv preprint arXiv:2107.03374 (2021)

Original

2021

Earlier work this paper cites.

Zhang, X., Sun, X., Luo, Y., Ji, J., Zhou, Y., Wu, Y., Huang, F., Ji, R.: Rstnet: Captioning with adaptive attention on visual and non-visual words. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15465–15474 (2021)

2021

Earlier work this paper cites.

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

Earlier work this paper cites.

Wang, B., Li, G., Zhou, X., Chen, Z., Grossman, T., Li, Y.: Screen2words: Automatic mobile ui summarization with multimodal learning. In: The 34th Annual ACM Symposium on User Interface Software and Technology, pp. 498–510 (2021)

2021

Cited alongside, same era.

Zhu, F., Lei, W., Huang, Y., Wang, C., Zhang, S., Lv, J., Feng, F., Chua, T.-S.: Tat-qa: A question answering benchmark on a hybrid of tabular and textual content in finance. arXiv preprint arXiv:2105.07624 (2021)

Original

2021

Cited alongside, same era.

Chen, Z., Chen, W., Smiley, C., Shah, S., Borova, I., Langdon, D., Moussa, R., Beane, M., Huang, T.-Y., Routledge, B., Wang, W.: Finqa: A dataset of numerical reasoning over financial data. Cornell University - arXiv,Cornell University - arXiv (2021)

2021

Cited alongside, same era.

Lu, P., Qiu, L., Chen, J., Xia, T., Zhao, Y., Zhang, W., Yu, Z., Liang, X., Zhu, S.-C.: Iconqa: A new benchmark for abstract diagram understanding and visual language reasoning. arXiv preprint arXiv:2110.13214 (2021)

Original

Dai, W., Li, J., Li, D., Tiong, A.M.H., Zhao, J., Wang, W., Li, B., Fung, P., Hoi, S.: InstructBLIP: Towards General-purpose Vision-Language Models with Instruction Tuning (2023)

2023

Later among the works it cites.

Chen, L., Li, S., Yan, J., Wang, H., Gunaratna, K., Yadav, V., Tang, Z., Srinivasan, V., Zhou, T., Huang, H., et al.: Alpagasus: Training a better alpaca with fewer data. arXiv preprint arXiv:2307.08701 (2023)

Original

2023

Later among the works it cites.

Cao, Y., Kang, Y., Sun, L.: Instruction mining: High-quality instruction data selection for large language models. arXiv preprint arXiv:2307.06290 (2023)

Original

2023

Later among the works it cites.

Wei, L., Jiang, Z., Huang, W., Sun, L.: Instructiongpt-4: A 200-instruction paradigm for fine-tuning minigpt-4. arXiv preprint arXiv:2308.12067 (2023)

Original

2023

Later among the works it cites.