The paper defines the OPS function that standardizes any CMBCP metric to its percentile rank in a reference distribution of possible performances, enabling consistent evaluation across different imbalance rates.
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Bayesian visual transformers with ensemble and sampling methods achieve a 7.4 percentage point gain on weighted F-beta score for affordance instance segmentation on the IIT-Aff dataset while providing calibrated epistemic and aleatoric uncertainty maps.
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Introducing the O-Value: A Universal Standardization for Confusion-Matrix-Based Classification Performance Metrics
The paper defines the OPS function that standardizes any CMBCP metric to its percentile rank in a reference distribution of possible performances, enabling consistent evaluation across different imbalance rates.
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Uncertainty Estimation in Instance Segmentation of Affordances via Bayesian Visual Transformers
Bayesian visual transformers with ensemble and sampling methods achieve a 7.4 percentage point gain on weighted F-beta score for affordance instance segmentation on the IIT-Aff dataset while providing calibrated epistemic and aleatoric uncertainty maps.