Exploring the Potential of VLMs in Remote Sensing through Prompt Optimization

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Examination shows improved detection and captioning performance in remote sensing, suggesting VLMs can better adapt to this domain.

Detection and captioning performance enhances with optimized prompts in remote sensing tasks, indicating a promising direction.
Prompt optimization leads to improved capabilities for the Vision-Language Models across remote sensing benchmarks.
Evaluation involves using two strategies: Zero-Shot Prompting and Prompt-Informed Supervised Fine-Tuning for better performance.
This research highlights the need for tailored prompting strategies to maximize VLM effectiveness in remote sensing applications.

Ma et al. (2025) studied this question.

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View Full Paper

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Authors

Examination shows improved detection and captioning performance in remote sensing, suggesting VLMs can better adapt to this domain.

Detection and captioning performance enhances with optimized prompts in remote sensing tasks, indicating a promising direction.
Prompt optimization leads to improved capabilities for the Vision-Language Models across remote sensing benchmarks.
Evaluation involves using two strategies: Zero-Shot Prompting and Prompt-Informed Supervised Fine-Tuning for better performance.
This research highlights the need for tailored prompting strategies to maximize VLM effectiveness in remote sensing applications.

Ma et al. (2025) studied this question.

View Full Paper

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