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ColorFM achieves a breakthrough in color transfer by seamlessly integrating optimization with learning, delivering unmatched visual quality and semantic fidelity.
DriftST achieves one-step generative inference of gene expression from H&E images, outperforming traditional methods that struggle with inter-gene dependencies and resolution flexibility.
A learned covisibility module in Argus effectively eliminates global pose drift, setting a new benchmark for 3D reconstruction accuracy in indoor scenes.
Agents can now escape the Self-Confirmation Trap, leading to more reliable experience learning and improved self-evolution.
