SLIP-RS introduces a Structured-Attribute Decoupling Paradigm with contrastive learning and a conformal reliability engine to create a 15M-attribute dataset for remote sensing pre-training.
S5: Scalable semi-supervised semantic segmentation in remote sens- ing.arXiv preprint arXiv:2508.12409,
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SLIP-RS: Structured-Attribute Language-Image Pre-Training for Remote Sensing Object Detection
SLIP-RS introduces a Structured-Attribute Decoupling Paradigm with contrastive learning and a conformal reliability engine to create a 15M-attribute dataset for remote sensing pre-training.