Mechanistic Interpretability-Guided Selective Fine-Tuning of Vision-Language Models for Centimeter-Level Flood Depth Estimation
Leveraging mechanistic interpretability to achieve centimeter-level flood depth estimation with minimal parameter overhead.
Urban flooding poses an escalating threat to transportation infrastructure, yet no operational system provides real-time, street-level flood-depth estimates at centimeter resolution. This paper presents three vision-language models fine-tuned for continuous flood-depth estimation...
ArXiv CS.CV
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This research demonstrates how mechanistic interpretability can surgically identify and tune the most critical layers of a vision-language model to achieve high-precision tasks. By reducing parameter requirements by up to 88% while maintaining extreme accuracy, the study provides a scalable pathway for deploying sophisticated AI models on resource-constrained edge hardware.
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