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Arxiv
3d
131
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Holistic Uncertainty Estimation For Open-Set Recognition
- Accurate uncertainty estimation is crucial for open-set recognition scenarios.
- The proposed HolUE method addresses uncertainty through a Bayesian probabilistic model.
- HolUE considers two sources of ambiguity: gallery uncertainty from overlapping classes and embedding uncertainty.
- Challenging datasets like IJB-C and VoxBlink were used to test HolUE, showing improved recognition error identification.
- Existing uncertainty estimation methods based solely on sample quality are outperformed by HolUE.
- HolUE introduces a holistic uncertainty estimation approach for open-set recognition.
- The method is designed to handle situations where a probe sample may belong to an unknown identity.
- Probabilistic embeddings play a role in determining sample quality for uncertainty estimation in open-set recognition.
- A new open-set recognition protocol for identification of whales and dolphins was introduced alongside HolUE.
- Bayesian probabilistic modeling forms the basis of the HolUE method for uncertainty estimation.
- The low variance of probabilistic embeddings may not always indicate low identification error probability in open-set recognition.
- HolUE performs well in scenarios where embeddings are close to multiple classes, leading to high uncertainty despite high sample quality.
- IJB-C and VoxBlink datasets were utilized to assess the effectiveness of HolUE.
- HolUE demonstrates superior recognition error identification compared to competing uncertainty estimation methods.
- Open-set recognition systems face challenges due to ambiguous gallery classes and embedding uncertainties.
- HolUE offers an improved approach for handling uncertainty in open-set recognition tasks.
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