CVPR 2023, highlight paper - Quantum Multi-Model Fitting

In this episode we discuss Quantum Multi-Model Fitting by Matteo Farina, Luca Magri, Willi Menapace, Elisa Ricci, Vladislav Golyanik, Federica Arrigoni. This paper introduces the first quantum approach to multi-model fitting (MMF), a fundamental computer vision problem. The authors propose a formulation that can be efficiently sampled on modern adiabatic quantum computers, without the relaxation of the objective function. They also propose an iterative and decomposed version of their method, which supports real-world-sized problems and show promising experimental results on various datasets. The source code is available on GitHub.