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The University of Queensland
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Decomposing holistic visual cues into subtle, spatially-associated discrepancies allows for state-of-the-art ultra-fine-grained classification even with limited training data.
Soybean leaves have intricate vein structures that unlock state-of-the-art ultra-fine-grained visual categorization, even with limited data.
Achieve SOTA wheat disease segmentation with limited data by cleverly combining the semantic power of DINOv2 with the geometric precision of SAM.
