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University of Electronic Science and Technology of China, Zhongguancun Academy
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User-level depression detection can be dramatically improved by routing individuals to specialized experts based on weak semantic priors, rather than relying on a one-size-fits-all classifier.
Conditional mixup bridges the gap between pseudo-labeling and contrastive learning, setting a new benchmark in sound event detection performance.
Meta-optimizing an AI data scientist can dramatically enhance the quality of synthetic datasets, outperforming traditional methods.
Reflectance variability in UAV multispectral imagery can exceed 137% across viewing angles, challenging assumptions about radiometric consistency in remote sensing.
PF-Trans achieves a remarkable PSNR of 48.50 dB, setting a new benchmark for spectral reconstruction in remote sensing.
Illumination variations can be tackled without prior metadata, enabling precise anomaly detection in dense forest environments.
Explicitly modeling occlusion intensity boosts UAV 3D object detection performance by over 9% in challenging environments.
Attention mechanisms in a two-stream framework boost classification accuracy of airborne multispectral point clouds by effectively integrating spatial-spectral features.
On-policy reward modeling with LLM judges not only unlocks significant performance gains on complex mathematical reasoning tasks, but also generalizes to improve performance on simpler numerical and multiple-choice benchmarks.
