1 code implementation • ECCV 2020 • Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel
Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.
no code implementations • 27 Jan 2023 • Oshri Halimi, Egor Larionov, Zohar Barzelay, Philipp Herholz, Tuur Stuyck
Our contribution is based on a simple observation: evaluating forces is computationally relatively cheap for traditional simulation methods and can be computed in parallel in contrast to their integration.
1 code implementation • 8 Sep 2022 • Amit Alfassy, Assaf Arbelle, Oshri Halimi, Sivan Harary, Roei Herzig, Eli Schwartz, Rameswar Panda, Michele Dolfi, Christoph Auer, Kate Saenko, PeterW. J. Staar, Rogerio Feris, Leonid Karlinsky
However, as we show in this paper, FMs still have poor out-of-the-box performance on expert tasks (e. g. retrieval of car manuals technical illustrations from language queries), data for which is either unseen or belonging to a long-tail part of the data distribution of the huge datasets used for FM pre-training.
Ranked #1 on Image-to-Text Retrieval on FETA Car-Manuals
no code implementations • 7 Jun 2022 • Oshri Halimi, Fabian Prada, Tuur Stuyck, Donglai Xiang, Timur Bagautdinov, He Wen, Ron Kimmel, Takaaki Shiratori, Chenglei Wu, Yaser Sheikh
Here, we propose an end-to-end pipeline for building drivable representations for clothing.
1 code implementation • ECCV 2020 • Luca Cosmo, Antonio Norelli, Oshri Halimi, Ron Kimmel, Emanuele Rodolà
In this paper, we advocate the adoption of metric preservation as a powerful prior for learning latent representations of deformable 3D shapes.
1 code implementation • 27 Jan 2020 • Oshri Halimi, Ido Imanuel, Or Litany, Giovanni Trappolini, Emanuele Rodolà, Leonidas Guibas, Ron Kimmel
Here, we claim that observing part of an object which was previously acquired as a whole, one could deal with both partial matching and shape completion in a holistic manner.
no code implementations • CVPR 2019 • Oshri Halimi, Or Litany, Emanuele Rodola, Alex M. Bronstein, Ron Kimmel
The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase.
1 code implementation • 6 Dec 2018 • Oshri Halimi, Or Litany, Emanuele Rodolà, Alex Bronstein, Ron Kimmel
The resulting learning model is class-agnostic, and is able to leverage any type of deformable geometric data for the training phase.