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Qualitative results on sample images from the new Q-MSEG dataset (96 qubits), where each color (symbol) represents a distinct planar motion. On average, the accuracies of our QuMoSeg-v1, Mode [5], Synch [4] and Xu et al. [63] are 0.97, 0.93, 0.93 and 0.89, respectively.


Motion segmentation is a challenging problem that seeks to identify independent motions in two or several input images. This paper introduces the first algorithm for motion segmentation that relies on adiabatic quantum optimization of the objective function. The proposed method achieves on-par performance with the state of the art on problem instances which can be mapped to modern quantum annealers.


  • Paper, 13.6 MB

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BibTeX, 1 KB

authors={Federica Arrigoni and Willi Menapace and Marcel Seelbach  Benkner and Elisa Ricci and Vladislav Golyanik}, 
title = {Quantum Motion Segmentation}, 
booktitle = {European Conference on Computer Vision (ECCV)}, 


For questions, clarifications, please get in touch with:
Federica Ariggoni
Vladislav Golyanik

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