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Self-training and tumor-aware deformations together boost brain tumor segmentation accuracy, outperforming traditional labeled-only approaches.
Clean performance metrics mislead expectations, as DINOv3 outperforms traditional models in real-world degradation scenarios.
Domain shifts can degrade vehicle attribute classification performance more than the choice of model architecture, revealing critical vulnerabilities in real-world applications.
Achieving an impressive 87.5% precision in recognizing ambivalence and hesitancy, this system redefines how we analyze emotional nuances in audio-text data.
