| Deep functional maps: Structured prediction for dense shape correspondence O Litany, T Remez, E Rodola, A Bronstein, M Bronstein Proceedings of the IEEE international conference on computer vision, 5659-5667, 2017 | 182 | 2017 |
| Learning to segment via cut-and-paste T Remez, J Huang, M Brown Proceedings of the European conference on computer vision (ECCV), 37-52, 2018 | 70 | 2018 |
| System and methods for risk management analysis of a pressure sensing system AB Shalom, S Hanassy, L Greenstein, T Remez US Patent App. 14/372,030, 2014 | 61 | 2014 |
| Efficient deformable shape correspondence via kernel matching M Vestner, Z Lähner, A Boyarski, O Litany, R Slossberg, T Remez, ... 2017 International Conference on 3D Vision (3DV), 517-526, 2017 | 58 | 2017 |
| Deep class-aware image denoising T Remez, O Litany, R Giryes, AM Bronstein 2017 international conference on sampling theory and applications (SampTA …, 2017 | 57 | 2017 |
| Deep convolutional denoising of low-light images T Remez, O Litany, R Giryes, AM Bronstein arXiv preprint arXiv:1701.01687, 2017 | 57 | 2017 |
| Class-aware fully convolutional Gaussian and Poisson denoising T Remez, O Litany, R Giryes, AM Bronstein IEEE Transactions on Image Processing 27 (11), 5707-5722, 2018 | 55 | 2018 |
| Pressure sensor assembly and associated method for preventing the development of pressure injuries D Weiss, R Poliakine-Baruchi, AB Shalom, L Greenstein, Y Assouline, ... US Patent App. 13/881,169, 2013 | 42 | 2013 |
| Into the wild with audioscope: Unsupervised audio-visual separation of on-screen sounds E Tzinis, S Wisdom, A Jansen, S Hershey, T Remez, DPW Ellis, ... arXiv preprint arXiv:2011.01143, 2020 | 20 | 2020 |
| A picture is worth a billion bits: Real-time image reconstruction from dense binary threshold pixels T Remez, O Litany, A Bronstein 2016 IEEE International Conference on Computational Photography (ICCP), 1-9, 2016 | 19* | 2016 |
| Asist: automatic semantically invariant scene transformation O Litany, T Remez, D Freedman, L Shapira, A Bronstein, R Gal Computer Vision and Image Understanding 157, 284-299, 2017 | 11 | 2017 |
| Efficient deformable shape correspondence via kernel matching Z Lähner, M Vestner, A Boyarski, O Litany, R Slossberg, T Remez, ... arXiv preprint arXiv:1707.08991, 2017 | 10 | 2017 |
| Cloud dictionary: Sparse coding and modeling for point clouds O Litany, T Remez, A Bronstein arXiv preprint arXiv:1612.04956, 2016 | 9 | 2016 |
| Shape correspondence with isometric and non-isometric deformations RM Dyke, C Stride, YK Lai, PL Rosin, M Aubry, A Boyarski, AM Bronstein, ... The Eurographics Association, 2019 | 8 | 2019 |
| Translatotron 2: Robust direct speech-to-speech translation Y Jia, MT Ramanovich, T Remez, R Pomerantz arXiv preprint arXiv:2107.08661, 2021 | 7 | 2021 |
| Methods and systems for the manufacture and initiation of a pressure detection mat TN Remez, AB Shalom, GY Averbuch US Patent 9,671,304, 2017 | 7 | 2017 |
| System and method for rapid data collection from pressure sensors in a pressure sensing system AB Shalom, I Raab, TN Remez, BB David, D Weiss, R Poliakine, ... US Patent 9,513,177, 2016 | 7 | 2016 |
| Or Litany, Raja Giryes, and Alex M Bronstein. Deep class-aware image denoising T Remez 2017 international conference on sampling theory and applications (SampTA …, 0 | 5 | |
| Improving On-Screen Sound Separation for Open-Domain Videos with Audio-Visual Self-Attention E Tzinis, S Wisdom, T Remez, JR Hershey arXiv preprint arXiv:2106.09669, 2021 | 4 | 2021 |
| White matter fiber representation using continuous dictionary learning G Alexandroni, Y Podolsky, H Greenspan, T Remez, O Litany, A Bronstein, ... International Conference on Medical Image Computing and Computer-Assisted …, 2017 | 4 | 2017 |
Or LitanyResearch Scientist, NVIDIAVerified email at nvidia.com
