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 |
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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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| author | Sabri Boughorbel Fethi Jarray Rachida Zegour Nauman Ullah Gilal Khaled Al Thelaya Marco Agus Jens Schneider |
| author_facet | Sabri Boughorbel Fethi Jarray Rachida Zegour Nauman Ullah Gilal Khaled Al Thelaya Marco Agus Jens Schneider |
| author_sort | Sabri Boughorbel |
| collection | DOAJ |
| description | 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 Optimization) that defines a loss function between a ground-truth density map and a target density map by modeling uncertainty over an augmented set of points. DUMLO formulates the loss function as a coupling between two optimal transport problems, which involves an unknown density map defined over the augmented points. To solve the problem, we propose a new algorithm, called Trihorn, which jointly estimates the loss function and the density map of the augmentation set. The latter can be interpreted as a measure of the uncertainty associated with the annotations. We provide a theoretical analysis and show that the generalization error bound of the proposed loss is tight. We extensively evaluate our model on benchmark datasets from three real-world applications: pathology cell counting, crowd counting and Vehicle Images Datasets. Our results demonstrate that the proposed model achieves good performance in terms of Mean Absolute Error and is robust to annotation noise while exhibiting a fast convergence property. |
| format | Article |
| id | doaj-art-46fa3ed693414c7ebc5b69e827d2945b |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-46fa3ed693414c7ebc5b69e827d2945b2025-08-24T11:30:32ZengNature PortfolioScientific Reports2045-23222025-08-0115111310.1038/s41598-025-14056-2Robust object counting through distribution uncertainty matching and optimal transportSabri Boughorbel0Fethi Jarray1Rachida Zegour2Nauman Ullah Gilal3Khaled Al Thelaya4Marco Agus5Jens Schneider6Qatar Computing Research Institute, Hamad Bin Khalifa UniversityLIMTIC Laboratory, UTM UniversityQatar Computing Research Institute, Hamad Bin Khalifa UniversityQatar Computing Research Institute, Hamad Bin Khalifa UniversityCollege of Science and Engineering, Hamad Bin Khalifa UniversityCollege of Science and Engineering, Hamad Bin Khalifa UniversityCollege of Science and Engineering, Hamad Bin Khalifa UniversityAbstract 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 Optimization) that defines a loss function between a ground-truth density map and a target density map by modeling uncertainty over an augmented set of points. DUMLO formulates the loss function as a coupling between two optimal transport problems, which involves an unknown density map defined over the augmented points. To solve the problem, we propose a new algorithm, called Trihorn, which jointly estimates the loss function and the density map of the augmentation set. The latter can be interpreted as a measure of the uncertainty associated with the annotations. We provide a theoretical analysis and show that the generalization error bound of the proposed loss is tight. We extensively evaluate our model on benchmark datasets from three real-world applications: pathology cell counting, crowd counting and Vehicle Images Datasets. Our results demonstrate that the proposed model achieves good performance in terms of Mean Absolute Error and is robust to annotation noise while exhibiting a fast convergence property.https://doi.org/10.1038/s41598-025-14056-2 |
| spellingShingle | Sabri Boughorbel Fethi Jarray Rachida Zegour Nauman Ullah Gilal Khaled Al Thelaya Marco Agus Jens Schneider Robust object counting through distribution uncertainty matching and optimal transport Scientific Reports |
| title | Robust object counting through distribution uncertainty matching and optimal transport |
| title_full | Robust object counting through distribution uncertainty matching and optimal transport |
| title_fullStr | Robust object counting through distribution uncertainty matching and optimal transport |
| title_full_unstemmed | Robust object counting through distribution uncertainty matching and optimal transport |
| title_short | Robust object counting through distribution uncertainty matching and optimal transport |
| title_sort | robust object counting through distribution uncertainty matching and optimal transport |
| url | https://doi.org/10.1038/s41598-025-14056-2 |
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