SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models
📰 ArXiv cs.AI
Learn to evaluate Vision-Language Models for remote sensing image analysis using SenseBench, a new benchmark for low-level visual perception and description
Action Steps
- Build a Vision-Language Model using a large dataset of remote sensing images
- Run SenseBench on the model to evaluate its low-level visual perception and description capabilities
- Configure the model to focus on physics-driven remote sensing degradations
- Test the model on various remote sensing tasks, such as image quality assessment and object detection
- Apply SenseBench to compare the performance of different Vision-Language Models
Who Needs to Know This
Researchers and developers working on Vision-Language Models and remote sensing applications can benefit from this benchmark to improve their models' performance and reliability
Key Insight
💡 SenseBench provides a diagnostic framework for evaluating Vision-Language Models' ability to characterize physics-driven remote sensing degradations
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🚀 Introducing SenseBench: a benchmark for evaluating Vision-Language Models in remote sensing image analysis #VisionLanguageModels #RemoteSensing
Key Takeaways
Learn to evaluate Vision-Language Models for remote sensing image analysis using SenseBench, a new benchmark for low-level visual perception and description
Full Article
Title: SenseBench: A Benchmark for Remote Sensing Low-Level Visual Perception and Description in Large Vision-Language Models
Abstract:
arXiv:2605.10576v1 Announce Type: cross Abstract: Low-level visual perception underpins reliable remote sensing (RS) image analysis, yet current image quality assessment (IQA) methods output uninterpretable scalar scores rather than characterizing physics-driven RS degradations, deviating markedly from the diagnostic needs of RS experts. While Vision-Language Models (VLMs) present a compelling alternative by delivering language-grounded IQA, their visual priors are heavily biased toward ground-l
Abstract:
arXiv:2605.10576v1 Announce Type: cross Abstract: Low-level visual perception underpins reliable remote sensing (RS) image analysis, yet current image quality assessment (IQA) methods output uninterpretable scalar scores rather than characterizing physics-driven RS degradations, deviating markedly from the diagnostic needs of RS experts. While Vision-Language Models (VLMs) present a compelling alternative by delivering language-grounded IQA, their visual priors are heavily biased toward ground-l
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