RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understanding

• URL: http://arxiv.org/abs/2406.12479v1
• Date: Tue, 18 Jun 2024 10:34:28 GMT
• Title: RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understanding
• Authors: Linrui Xu, Ling Zhao, Wang Guo, Qiujun Li, Kewang Long, Kaiqi Zou, Yuhan Wang, Haifeng Li,
• Abstract summary: Under the new LaGD paradigm, the old datasets are no longer suitable for fire-new tasks. We designed a high-quality, diversified, and unified multimodal instruction-following dataset for RSI understanding. The empirical results show that the fine-tuned MLLMs by RS-GPT4V can describe fine-grained information.
• Score: 4.266920365127677
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The remote sensing image intelligence understanding model is undergoing a new profound paradigm shift which has been promoted by multi-modal large language model (MLLM), i.e. from the paradigm learning a domain model (LaDM) shifts to paradigm learning a pre-trained general foundation model followed by an adaptive domain model (LaGD). Under the new LaGD paradigm, the old datasets, which have led to advances in RSI intelligence understanding in the last decade, are no longer suitable for fire-new tasks. We argued that a new dataset must be designed to lighten tasks with the following features: 1) Generalization: training model to learn shared knowledge among tasks and to adapt to different tasks; 2) Understanding complex scenes: training model to understand the fine-grained attribute of the objects of interest, and to be able to describe the scene with natural language; 3) Reasoning: training model to be able to realize high-level visual reasoning. In this paper, we designed a high-quality, diversified, and unified multimodal instruction-following dataset for RSI understanding produced by GPT-4V and existing datasets, which we called RS-GPT4V. To achieve generalization, we used a (Question, Answer) which was deduced from GPT-4V via instruction-following to unify the tasks such as captioning and localization; To achieve complex scene, we proposed a hierarchical instruction description with local strategy in which the fine-grained attributes of the objects and their spatial relationships are described and global strategy in which all the local information are integrated to yield detailed instruction descript; To achieve reasoning, we designed multiple-turn QA pair to provide the reasoning ability for a model. The empirical results show that the fine-tuned MLLMs by RS-GPT4V can describe fine-grained information. The dataset is available at: https://github.com/GeoX-Lab/RS-GPT4V.

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