Ranking vs. Assignment: The Metric Mismatch in Multi-View Object Association
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914622828707840 |
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| author | Shelukhan, Matvei Mamedov, Timur Chukhrov, Aleksandr Kvanchiani, Karina |
| author_facet | Shelukhan, Matvei Mamedov, Timur Chukhrov, Aleksandr Kvanchiani, Karina |
| contents | Multi-view object association is an important computer vision problem that underlies many multi-camera perception tasks. While this task is naturally formulated as a constrained one-to-one matching problem, recent works heavily rely on pairwise ranking metrics like AP and FPR-95 for model evaluation. We highlight a fundamental mismatch between these metrics and the actual assignment objective. Theoretically, we show that AP and FPR-95 can be imperfect even when the assignment is already correct, and that Sinkhorn-based normalization can make them perfect. Conversely, optimal pairwise ranking can still lead to incorrect assignments. We validate this mismatch in practice by using our Sinkhorn-based normalization as a controlled post-processing stress test. We show that optimizing just a few post-processing parameters significantly boosts AP and FPR-95 without corresponding improvements in assignment-level metrics such as ACC and IPAA. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2606_02022 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Ranking vs. Assignment: The Metric Mismatch in Multi-View Object Association Shelukhan, Matvei Mamedov, Timur Chukhrov, Aleksandr Kvanchiani, Karina Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Multi-view object association is an important computer vision problem that underlies many multi-camera perception tasks. While this task is naturally formulated as a constrained one-to-one matching problem, recent works heavily rely on pairwise ranking metrics like AP and FPR-95 for model evaluation. We highlight a fundamental mismatch between these metrics and the actual assignment objective. Theoretically, we show that AP and FPR-95 can be imperfect even when the assignment is already correct, and that Sinkhorn-based normalization can make them perfect. Conversely, optimal pairwise ranking can still lead to incorrect assignments. We validate this mismatch in practice by using our Sinkhorn-based normalization as a controlled post-processing stress test. We show that optimizing just a few post-processing parameters significantly boosts AP and FPR-95 without corresponding improvements in assignment-level metrics such as ACC and IPAA. |
| title | Ranking vs. Assignment: The Metric Mismatch in Multi-View Object Association |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2606.02022 |