Summary of Rs-gpt4v: a Unified Multimodal Instruction-following Dataset For Remote Sensing Image Understanding, by Linrui Xu et al.
RS-GPT4V: A Unified Multimodal Instruction-Following Dataset for Remote Sensing Image Understanding
by Linrui Xu, Ling Zhao, Wang Guo, Qiujun Li, Kewang Long, Kaiqi Zou, Yuhan Wang, Haifeng Li
First submitted to arxiv on: 18 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The remote sensing image intelligence understanding model is undergoing a significant shift, driven by multi-modal large language models (MLLMs). The new paradigm, referred to as learning an adaptive domain model (LaGD), replaces the previous learning a domain model (LaDM) approach. This shift requires new datasets that can handle fire-new tasks with features such as generalization, understanding complex scenes, and reasoning. A high-quality, diversified, and unified multimodal instruction-following dataset called RS-GPT4V is designed to achieve these goals. The dataset is produced by GPT-4V and existing datasets, and its creation involves (Question, Answer) pairs, hierarchical instruction descriptions, and multiple-turn QA pairs. The fine-tuned MLLMs using RS-GPT4V can describe fine-grained information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The remote sensing image intelligence understanding model is getting a big update thanks to new language models. This change will help the model learn from many different tasks and understand complex scenes better. To make this work, scientists created a new dataset that includes lots of examples for the model to practice with. The dataset has special features like generalization, complex scene understanding, and reasoning. It’s designed to help the model get even better at understanding what it sees. |
Keywords
» Artificial intelligence » Generalization » Gpt » Multi modal » Scene understanding
