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GeoFlow reveals that integrating geospatial attributes can dramatically enhance the accuracy and diversity of urban mobility predictions.
BETA achieves state-of-the-art continual learning performance with just 0.05 million trainable parameters, outperforming traditional methods by 180–3000 times in parameter efficiency.
One-step diffusion for video face restoration is now viable, achieving state-of-the-art results by learning separate spatial and temporal priors.
