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Nowadays, the research on Large Vision-Language Models (LVLMs) has been significantly promoted thanks to the success of Large Language Models (LLM).
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13
2014
Earlier work this paper cites.
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. L. Zitnick, and D. Parikh, “Vqa: Visual question answering,” in Proceedings of the IEEE international conference on computer vision
2015
Earlier work this paper cites.
Y. Goyal, T. Khot, D. Summers-Stay, D. Batra, and D. Parikh, “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
2017
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2018
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2018
Earlier work this paper cites.
H. Agrawal, K. Desai, Y. Wang, X. Chen, R. Jain, M. Johnson, D. Batra, D. Parikh, S. Lee, and P. Anderson, “Nocaps: Novel object captioning at scale,” in Proceedings of the IEEE/CVF international conference on computer vision
2019
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” The Journal of Machine Learning Research
2020
Earlier work this paper cites.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al
2020
Earlier work this paper cites.
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
2022
Cited alongside, same era.
J. Li, D. Li, C. Xiong, and S. Hoi, “Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation,” in International Conference on Machine Learning
2022
Cited alongside, same era.
Z. Gan, L. Li, C. Li, L. Wang, Z. Liu, J. Gao, et al
2022
Cited alongside, same era.
M. Jia, L. Tang, B.-C. Chen, C. Cardie, S. Belongie, B. Hariharan, and S.-N. Lim, “Visual prompt tuning,” in European Conference on Computer Vision
2022
Cited alongside, same era.
M. Stefanini, M. Cornia, L. Baraldi, S. Cascianelli, G. Fiameni, and R. Cucchiara, “From show to tell: A survey on deep learning-based image captioning,” IEEE transactions on pattern analysis and machine intelligence
2023
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H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” arXiv preprint arXiv:2304.08485
2023
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2023
Closest in time.
2023
Closest in time.
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2022
Cited alongside, same era.
P. Lu, S. Mishra, T. Xia, L. Qiu, K.-W. Chang, S.-C. Zhu, O. Tafjord, P. Clark, and A. Kalyan, “Learn to explain: Multimodal reasoning via thought chains for science question answering,” Advances in Neural Information Processing Systems
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2023
Cited alongside, same era.
OpenAI, “Gpt-4 technical report,” 2023
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
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C. Fu, P. Chen, Y. Shen, Y. Qin, M. Zhang, X. Lin, Z. Qiu, W. Lin, J. Yang, X. Zheng, et al
2023
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L. Zheng, W.-L. Chiang, Y. Sheng, S. Zhuang, Z. Wu, Y. Zhuang, Z. Lin, Z. Li, D. Li, E. Xing, et al
2023
Closest in time.