GeoSeg-OV outperforms prior open-vocabulary remote sensing segmentation methods by 2.5 to 2.7 average mIoU on a new seven-dataset benchmark by repurposing auxiliary vision foundation models as structural guidance rather than as additional visual-text matchers.
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , pages=
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GeoSeg-OV: Bridging Geospatial Gaps with Structural Guidance for Open-Vocabulary Remote Sensing Segmentation
GeoSeg-OV outperforms prior open-vocabulary remote sensing segmentation methods by 2.5 to 2.7 average mIoU on a new seven-dataset benchmark by repurposing auxiliary vision foundation models as structural guidance rather than as additional visual-text matchers.