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Restrict, Don't Retrain: Inference-Time VLM Guidance for Zero-Shot Aerial Segmentation

arXiv · AI, language, vision and robotics · article · Sep 1, 2026 · UTC

Global welfare often depends on the correct interpretation of aerial and satellite imagery. Acting on such imagery (mapping flooded ground, crop extent, or damaged infrastructure) demands pixel-level segmentation to ensure perfect class localization. Pretrained general foundation models, when applied directly, often miss important features and cannot always find all the classes belonging to a given scene, overlooking smaller objects that matter most. We use a single consumer-grade GPU running a vision-language model (VLM) to supply this missing guidance, improving segmentation while producing

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