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Calibrated uncertainty estimates from YOLO-Pose models enable reliable keypoint ranking and effective pruning, transforming how we assess localization confidence.
Forget RNNs for modeling dynamics: a Kalman filter-VAE hybrid can learn state-space representations that disentangle static and dynamic features, outperforming previous models in prediction accuracy and system identification.
