Point&Grasp probabilistically integrates pointing and grasp gestures for out-of-reach object selection in MR, trained on a new ORG dataset, and outperforms single-cue baselines in user studies.
https://arxiv.org/abs/ 1902.06977
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Ensemble-based method of moments on softmax outputs produces stable Dirichlet predictive distributions that improve uncertainty-guided tasks like selective classification over evidential deep learning.
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Point & Grasp: Flexible Selection of Out-of-Reach Objects Through Probabilistic Cue Integration
Point&Grasp probabilistically integrates pointing and grasp gestures for out-of-reach object selection in MR, trained on a new ORG dataset, and outperforms single-cue baselines in user studies.
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Ensemble-Based Dirichlet Modeling for Predictive Uncertainty and Selective Classification
Ensemble-based method of moments on softmax outputs produces stable Dirichlet predictive distributions that improve uncertainty-guided tasks like selective classification over evidential deep learning.