GeoRSMLLM: A Multimodal Large Language Model for Vision-Language Tasks in Geoscience and Remote Sensing

Y. Qiao, C. Deng, and Q. Wu, “Referring expression comprehension: A survey of methods and datasets,” 2020. [Online]. Available: https://arxiv.org/abs/2007.09554

Original

2007

Earlier work this paper cites.

I. Demir, K. Koperski, D. Lindenbaum, G. Pang, J. Huang, S. Basu, F. Hughes, D. Tuia, and R. Raskar, “Deepglobe 2018: A challenge to parse the earth through satellite images,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2018, pp. 172–17 209

2018

Earlier work this paper cites.

S. Waqas Zamir, A. Arora, A. Gupta, S. Khan, G. Sun, F. Shahbaz Khan, F. Zhu, L. Shao, G.-S. Xia, and X. Bai, “isaid: A large-scale dataset for instance segmentation in aerial images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 28–37

2019

Earlier work this paper cites.

A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” 2021

2021

Earlier work this paper cites.

J. Li, R. R. Selvaraju, A. D. Gotmare, S. Joty, C. Xiong, and S. Hoi, “Align before fuse: Vision and language representation learning with momentum distillation,” 2021

2021

Earlier work this paper cites.

J. Wang, Z. Zheng, A. Ma, X. Lu, and Y. Zhong, “Loveda: A remote sensing land-cover dataset for domain adaptive semantic segmentation,” in Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks , J. Vanschoren and S. Yeung, Eds., vol. 1. Curran Associates, Inc., 2021. [Online]. Available: https://datasets-benchmarks-proceedings.neurips.cc/paper_files/paper/2021/file/4e732ced3463d06de0ca9a15b6153677-Paper-round2.pdf

2021

Earlier work this paper cites.

Y. Li, F. Liang, L. Zhao, Y. Cui, W. Ouyang, J. Shao, F. Yu, and J. Yan, “Supervision exists everywhere: A data efficient contrastive language-image pre-training paradigm,” 2022

2022

Earlier work this paper cites.

Z. Yuan, W. Zhang, C. Li, Z. Pan, Y. Mao, J. Chen, S. Li, H. Wang, and X. Sun, “Learning to evaluate performance of multimodal semantic localization,” IEEE Transactions on Geoscience and Remote Sensing , vol. 60, pp. 1–18, 2022. [Online]. Available: https://doi.org/10.1109%2Ftgrs.2022.3207171

2022

Earlier work this paper cites.

S. Liu, Z. Zeng, T. Ren, F. Li, H. Zhang, J. Yang, C. Li, J. Yang, H. Su, J. Zhu et al. , “Grounding dino: Marrying dino with grounded pre-training for open-set object detection,” arXiv preprint arXiv:2303.05499 , 2023

Original

2023

Earlier work this paper cites.

H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,” 2023

2023

Earlier work this paper cites.

J. Chen, D. Zhu, X. Shen, X. Li, Z. Liu, P. Zhang, R. Krishnamoorthi, V. Chandra, Y. Xiong, and M. Elhoseiny, “Minigpt-v2: large language model as a unified interface for vision-language multi-task learning,” 2023. [Online]. Available: https://arxiv.org/abs/2310.09478

Original

2023

Earlier work this paper cites.

R. Girdhar, A. El-Nouby, Z. Liu, M. Singh, K. V. Alwala, A. Joulin, and I. Misra, “Imagebind: One embedding space to bind them all,” 2023. [Online]. Available: https://arxiv.org/abs/2305.05665

Original

2023

Earlier work this paper cites.

C. Xie, Z. Zhang, Y. Wu, F. Zhu, R. Zhao, and S. Liang, “Described object detection: Liberating object detection with flexible expressions,” in Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS) , 2023

2023

Earlier work this paper cites.

H. Liu, C. Li, Y. Li, and Y. J. Lee, “Improved baselines with visual instruction tuning,” 2023

2023

Earlier work this paper cites.

W. Wang, Z. Chen, X. Chen, J. Wu, X. Zhu, G. Zeng, P. Luo, T. Lu, J. Zhou, Y. Qiao, and J. Dai, “Visionllm: Large language model is also an open-ended decoder for vision-centric tasks,” 2023. [Online]. Available: https://arxiv.org/abs/2305.11175

Original

2023

Cited alongside, same era.

A. Zhang, W. Ji, and T.-S. Chua, “Next-chat: An lmm for chat, detection and segmentation,” 2023

2023

Cited alongside, same era.

V. V. Cepeda, G. K. Nayak, and M. Shah, “Geoclip: Clip-inspired alignment between locations and images for effective worldwide geo-localization,” 2023. [Online]. Available: https://arxiv.org/abs/2309.16020

Original

2023

Cited alongside, same era.

X. Zhai, B. Mustafa, A. Kolesnikov, and L. Beyer, “Sigmoid loss for language image pre-training,” 2023. [Online]. Available: https://arxiv.org/abs/2303.15343

Original

2023

Cited alongside, same era.

J. Zhang, J. Huang, S. Jin, and S. Lu, “Vision-language models for vision tasks: A survey,” 2024. [Online]. Available: https://arxiv.org/abs/2304.00685

K. Kuckreja, M. S. Danish, M. Naseer, A. Das, S. Khan, and F. S. Khan, “Geochat: Grounded large vision-language model for remote sensing,” The IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2024

2024

Later among the works it cites.

W. Zhang, M. Cai, T. Zhang, Y. Zhuang, and X. Mao, “Earthgpt: A universal multi-modal large language model for multi-sensor image comprehension in remote sensing domain,” 2024. [Online]. Available: https://arxiv.org/abs/2401.16822

Original

2024

Later among the works it cites.

C. Pang, J. Wu, J. Li, Y. Liu, J. Sun, W. Li, X. Weng, S. Wang, L. Feng, G.-S. Xia, and C. He, “H2rsvlm: Towards helpful and honest remote sensing large vision language model,” 2024. [Online]. Available: https://arxiv.org/abs/2403.20213

Original

2024

Later among the works it cites.

X. Li, J. Ding, and M. Elhoseiny, “Vrsbench: A versatile vision-language benchmark dataset for remote sensing image understanding,” 2024. [Online]. Available: https://arxiv.org/abs/2406.12384

Original

2024

Later among the works it cites.