Robust object counting through distribution uncertainty matching and optimal transport
Abstract Object counting can be formulated as a density estimation task using point-annotated images. Although such labeling is cost-effective, trained models can be sensitive to annotation noise. In this paper, we propose a method called DUMLO (Distribution Uncertainty Matching for Loss Optimizatio...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-08-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-025-14056-2 |
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