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Late visual-token updates can be safely ignored, leading to a 33.7% reduction in computational load without sacrificing performance.
Stop fragmented land cover predictions: SSDM leverages global geospatial embeddings to guide local feature extraction, achieving state-of-the-art performance in high-resolution remote sensing mapping.
Smashed data bottlenecks in split learning? SL-FAC shrinks communication overhead by intelligently quantizing frequency components, boosting training efficiency.
