no code implementations • 15 Dec 2021 • Amit Bracha, Noam Rotstein, David Bensaïd, Ron Slossberg, Ron Kimmel
To mitigate this quadratic relation, we propose a simple but effective method that uses a refinement network for depth estimation.
no code implementations • 10 Oct 2021 • Ron Slossberg, Ibrahim Jubran, Ron Kimmel
In this paper, we propose a novel unified pipeline for both tasks, generation of both geometry and texture, and recovery of high-fidelity texture.
no code implementations • 23 Dec 2020 • Ron Slossberg, Oron Anschel, Amir Markovitz, Ron Litman, Aviad Aberdam, Shahar Tsiper, Shai Mazor, Jon Wu, R. Manmatha
Although the topic of confidence calibration has been an active research area for the last several decades, the case of structured and sequence prediction calibration has been scarcely explored.
2 code implementations • CVPR 2021 • Aviad Aberdam, Ron Litman, Shahar Tsiper, Oron Anschel, Ron Slossberg, Shai Mazor, R. Manmatha, Pietro Perona
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition.
no code implementations • 19 Jan 2019 • Gil Shamai, Ron Slossberg, Ron Kimmel
We circumvent the parametrization issue by imposing a global mapping from our data to the unit rectangle.
no code implementations • 24 Aug 2018 • Ron Slossberg, Gil Shamai, Ron Kimmel
A GAN is employed in order to imitate the space of parametrized human textures, while corresponding facial geometries are generated by learning the best 3DMM coefficients for each texture.
Computational Geometry
1 code implementation • 25 Jul 2017 • Zorah Lähner, Matthias Vestner, Amit Boyarski, Or Litany, Ron Slossberg, Tal Remez, Emanuele Rodolà, Alex Bronstein, Michael Bronstein, Ron Kimmel, Daniel Cremers
We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.
no code implementations • 6 Dec 2016 • Ron Slossberg, Aaron Wetzler, Ron Kimmel
Stereo reconstruction from rectified images has recently been revisited within the context of deep learning.
no code implementations • 21 Jul 2015 • Aaron Wetzler, Ron Slossberg, Ron Kimmel
We investigate a novel global orientation regression approach for articulated objects using a deep convolutional neural network.