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cs.CV2026
Bridging the Gap between Labeled and Unlabeled Data via Unified Flow with Feature Memory Bank
Shanwen Wang, Xin Sun, Danfeng Hong +2
Although semi-supervised semantic segmentation () utilizes abundant unlabeled data to reduce manual labeling burdens, independent training of labeled and unlabeled data…
cs.CV2026
Frequency and Edge-Guided Segment Anything Model for Remote Sensing Image Semantic Segmentation
Feng Gao, Zizhe Pan, Haoting Wang +4
Remote sensing image semantic segmentation (RSISS) has attracted significant attention due to the growing demand for fine-grained land cover information. The Segment Anything Model…