EMDUL expands mmWave HPE datasets via pseudo-labeling of unlabeled data and a closed-form LiDAR-to-mmWave converter, reducing pose estimation errors by 15.1% in-domain and 18.9% out-of-domain.
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ViewBridge uses curriculum knowledge distillation and a geometry-based occlusion metric to learn view-invariant activity representations from multi-view training data, outperforming prior methods on keystep grounding and recognition across three datasets from single-view inference.
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Expanding mmWave Datasets for Human Pose Estimation with Unlabeled Data and LiDAR Datasets
EMDUL expands mmWave HPE datasets via pseudo-labeling of unlabeled data and a closed-form LiDAR-to-mmWave converter, reducing pose estimation errors by 15.1% in-domain and 18.9% out-of-domain.
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ViewBridge: Curriculum Knowledge Distillation for Activity View-Invariance Under Extreme Viewpoint Changes
ViewBridge uses curriculum knowledge distillation and a geometry-based occlusion metric to learn view-invariant activity representations from multi-view training data, outperforming prior methods on keystep grounding and recognition across three datasets from single-view inference.