# A Methodology to Evaluate Strategies Predicting Rankings on Unseen Domains

arXiv: https://arxiv.org/abs/2505.15595 · HTML: https://mlsanity.com/paper/2505.15595/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 28 references that are in the map.
- First posted on arXiv 21 May 2025.

## Builds on (most cited first, 11 of 11)

- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [COCO-Stuff: Thing and Stuff Classes in Context](https://mlsanity.com/paper/1612.03716/) (2016)
- [The frontier of simulation-based inference](https://mlsanity.com/paper/1911.01429/) (2019)
- [Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation](https://mlsanity.com/paper/1811.05255/) (2018)
- [Real-Time Semantic Background Subtraction](https://mlsanity.com/paper/2002.04993/) (2020)
- [Summarizing the performances of a background subtraction algorithm measured on several videos](https://mlsanity.com/paper/2002.05654/) (2020)
- [Foundations of the Theory of Performance-Based Ranking](https://mlsanity.com/paper/2412.04227/) (2024)
- [The Tile: A 2D Map of Ranking Scores for Two-Class Classification](https://mlsanity.com/paper/2412.04309/) (2024)
- [A Hitchhiker's Guide to Understanding Performances of Two-Class Classifiers](https://mlsanity.com/paper/2412.04377/) (2024)
- [An exploration of the performances achievable by combining unsupervised background subtraction algorithms](https://mlsanity.com/paper/2202.12563/) (2022)
- [Physically Interpretable Probabilistic Domain Characterization](https://mlsanity.com/paper/2411.14827/) (2024)

## Built on by (most cited first, 1 of 1)

- [Event Detection in Videos: A Framework for the Development of New Methods](https://mlsanity.com/paper/2607.04372/) (2026)

## Abstract (arXiv)

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).