A Methodology to Evaluate Strategies Predicting Rankings on Unseen Domains
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914415290351616 |
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| author | Piérard, Sébastien Deliège, Adrien Halin, Anaïs Van Droogenbroeck, Marc |
| author_facet | Piérard, Sébastien Deliège, Adrien Halin, Anaïs Van Droogenbroeck, Marc |
| contents | Frequently, multiple entities (methods, algorithms, procedures, solutions, etc.) can be developed for a common task and applied across various domains that differ in the distribution of scenarios encountered. For example, in computer vision, the input data provided to image analysis methods depend on the type of sensor used, its location, and the scene content. However, a crucial difficulty remains: can we predict which entities will perform best in a new domain based on assessments on known domains, without having to carry out new and costly evaluations? This paper presents an original methodology to address this question, in a leave-one-domain-out fashion, for various application-specific preferences. We illustrate its use with 30 strategies to predict the rankings of 40 entities (unsupervised background subtraction methods) on 53 domains (videos). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_15595 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Methodology to Evaluate Strategies Predicting Rankings on Unseen Domains Piérard, Sébastien Deliège, Adrien Halin, Anaïs Van Droogenbroeck, Marc Performance Computer Vision and Pattern Recognition Frequently, multiple entities (methods, algorithms, procedures, solutions, etc.) can be developed for a common task and applied across various domains that differ in the distribution of scenarios encountered. For example, in computer vision, the input data provided to image analysis methods depend on the type of sensor used, its location, and the scene content. However, a crucial difficulty remains: can we predict which entities will perform best in a new domain based on assessments on known domains, without having to carry out new and costly evaluations? This paper presents an original methodology to address this question, in a leave-one-domain-out fashion, for various application-specific preferences. We illustrate its use with 30 strategies to predict the rankings of 40 entities (unsupervised background subtraction methods) on 53 domains (videos). |
| title | A Methodology to Evaluate Strategies Predicting Rankings on Unseen Domains |
| topic | Performance Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.15595 |