RS-OVC is the first open-vocabulary counting model for remote-sensing imagery that enables accurate counts of novel object classes unseen during training via textual or visual conditioning.
IJCV88(2), 303–338 (2010)
2 Pith papers cite this work. Polarity classification is still indexing.
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OBBSeg segments irregular medical lesions from oriented bounding-box labels via a Mask-to-OBB loss and prompt modules, claiming near fully-supervised accuracy across 13 datasets and 5 modalities.
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RS-OVC: Open-Vocabulary Counting for Remote-Sensing Data
RS-OVC is the first open-vocabulary counting model for remote-sensing imagery that enables accurate counts of novel object classes unseen during training via textual or visual conditioning.
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OBBSeg: Irregular Lesion Segmentation under Oriented Bounding Box Annotations
OBBSeg segments irregular medical lesions from oriented bounding-box labels via a Mask-to-OBB loss and prompt modules, claiming near fully-supervised accuracy across 13 datasets and 5 modalities.