Selected Publications

We introduce a graph matching method that can account for constraints of arbitrary order, with arbitrary potential functions. Unlike previous decomposition approaches that rely on the graph structures, we introduce a decomposition of the matching constraints. Graph matching is then reformulated as a non-convex non-separable optimization problem that can be split into smaller and much-easier-to-solve subproblems, by means of the alternating direction method of multipliers. The proposed framework is modular, scalable, and can be instantiated into different variants. Two instantiations are studied exploring pairwise and higher-order constraints. Experimental results on widely adopted benchmarks involving synthetic and real examples demonstrate that the proposed solutions outperform existing pairwise graph matching methods, and competitive with the state of the art in higher-order settings.
In CVPR (2017)

Recent Publications


I have been a teaching assistant for the following courses at CentraleSupélec and ENS Paris-Saclay:

  • 2017-2018: Discrete Inference and Learning
  • 2016-2017: Introduction to Deep Learning
  • 2015-2016: Machine Learning for Computer Vision
  • 2014-2015: Machine Learning for Computer Vision


  • [email protected]
  • Center for Visual Computing, CentraleSupélec, 3 rue Joliot-Curie, 91192 Gif-sur-Yvette, France