25 citations · 45 across the 7 of their papers we have counts for
9 papers
Depth Refinement for Improved Stereo Reconstruction
Amit Bracha, Noam Rotstein, David Bensaïd +2
Depth estimation is a cornerstone of a vast number of applications requiring 3D assessment of the environment, such as robotics, augmented reality, and autonomous driving to name a…
Unsupervised High-Fidelity Facial Texture Generation and Reconstruction
Ron Slossberg, Ibrahim Jubran, Ron Kimmel
Many methods have been proposed over the years to tackle the task of facial 3D geometry and texture recovery from a single image. Such methods often fail to provide high-fidelity t…
On Calibration of Scene-Text Recognition Models
Ron Slossberg, Oron Anschel, Amir Markovitz +6
In this work, we study the problem of word-level confidence calibration for scene-text recognition (STR). Although the topic of confidence calibration has been an active research a…
Sequence-to-Sequence Contrastive Learning for Text Recognition
Aviad Aberdam, Ron Litman, Shahar Tsiper +5
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition. To account for the sequence-to-sequence…
Synthesizing facial photometries and corresponding geometries using generative adversarial networks
Gil Shamai, Ron Slossberg, Ron Kimmel
Artificial data synthesis is currently a well studied topic with useful applications in data science, computer vision, graphics and many other fields. Generating realistic data is…
High Quality Facial Surface and Texture Synthesis via Generative Adversarial Networks
Ron Slossberg, Gil Shamai, Ron Kimmel
In the past several decades, many attempts have been made to model synthetic realistic geometric data. The goal of such models is to generate plausible 3D geometries and textures.…