Naver Corporation

Romain Brégier

Boris Chidlovskii

Philippe Weinzaepfel

Vincent Leroy

Jérôme Revaud

Yohann Cabon

Leonid Antsfeld

Gabriela Csurka Khedari

Thomas Lucas

A method includes: performing unsupervised pre-training of a model, the model including and a decoder including: obtaining a first image and a second image under different conditions or from different viewpoints; encoding, by the encoder, the first image into a representation of the first image and the second image into a representation of the second image; transforming the representation of the first image into a transformed representation; decoding, by the decoder, the transformed representation into a reconstructed image, where the transforming of the representation of the first image and the decoding of the transformed representation is based on the representation of the first image and the representation of the second image; and adjusting one or more parameters of at least one of the encoder and the decoder based on minimizing a loss; and fine-tuning the model, initialized with a set of task specific encoder parameters, for a geometric vision task.

Unsupervised pre-training of geometric vision models

Growing community of inventors

Grenoble, France

Romain Brégier