Summary of Advancements in Visual Language Models For Remote Sensing: Datasets, Capabilities, and Enhancement Techniques, by Lijie Tao et al.
Advancements in Visual Language Models for Remote Sensing: Datasets, Capabilities, and Enhancement Techniques
by Lijie Tao, Haokui Zhang, Haizhao Jing, Yu Liu, Dawei Yan, Guoting Wei, Xizhe Xue
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 A recent surge in interest in artificial intelligence (AI) has been driven by the impressive performance of ChatGPT, and visual language models (VLMs) have taken center stage. Unlike traditional AI approaches that formulate tasks as discriminative models, VLMs frame tasks as generative models, enabling the handling of more complex problems. The remote sensing (RS) field has also adopted this trend, introducing several VLM-based RS methods that have shown promising performance and vast potential. This paper reviews fundamental theories related to VLMs, summarizes datasets constructed for VLMs in RS, and categorizes improvement methods into three parts based on core components of VLMs. A comparison of these methods is also provided. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI has become increasingly popular since ChatGPT’s remarkable success. Visual language models (VLMs) are a new trend that’s taking over the AI world. Instead of solving problems one way, VLMs try to generate solutions. This approach works well for complex tasks. The remote sensing field is also getting in on the action, using VLMs to solve problems and get better results. |
