Summary of Skysensegpt: a Fine-grained Instruction Tuning Dataset and Model For Remote Sensing Vision-language Understanding, by Junwei Luo et al.
SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding
by Junwei Luo, Zhen Pang, Yongjun Zhang, Tingzhu Wang, Linlin Wang, Bo Dang, Jiangwei Lao, Jian Wang, Jingdong Chen, Yihua Tan, Yansheng Li
First submitted to arxiv on: 14 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 This paper focuses on developing Large Multi-Modal Models (LMMs) for remote sensing imagery comprehension. Existing LMMs have limitations in understanding complex semantic relations among objects in remote sensing scenes due to the shortcomings of existing datasets. To address this, the authors propose a large-scale instruction tuning dataset called FIT-RS, containing 1,800,851 instruction samples that cover common interpretation tasks and introduce several complex comprehension tasks. The authors build the FIT-RSFG benchmark based on this dataset and establish a new benchmark for evaluating fine-grained relation comprehension capabilities of LMMs, named FIT-RSRC. They also propose SkySenseGPT, which achieves outstanding performance on both public datasets and FIT-RSFG, surpassing existing LMMs. The authors hope that the FIT-RS dataset can enhance the relation comprehension capability of LMMs and provide a large-scale fine-grained data source for the remote sensing community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about making computers better at understanding images taken from space. Right now, these computer models are limited because they don’t have enough training data to learn how to understand complex scenes. The authors created a new dataset with lots of examples that help teach these models to recognize objects and their relationships in those complex scenes. They also built a special tool to test the models’ abilities to comprehend these scenes. This research can help improve computers’ ability to analyze remote sensing images, which is important for many applications like environmental monitoring and natural disaster response. |
Keywords
» Artificial intelligence » Instruction tuning » Multi modal
