1 code implementation • 29 Oct 2023 • Alon Shoshan, Nadav Bhonker, Emanuel Ben Baruch, Ori Nizan, Igor Kviatkovsky, Joshua Engelsma, Manoj Aggarwal, Gerard Medioni
We demonstrate the merits of FPGAN-Control, both quantitatively and qualitatively, in terms of identity preservation level, degree of appearance control, and low synthetic-to-real domain gap.
1 code implementation • ICCV 2021 • Avi Ben-Cohen, Nadav Zamir, Emanuel Ben Baruch, Itamar Friedman, Lihi Zelnik-Manor
We argue that using a single embedding vector to represent an image, as commonly practiced, is not sufficient to rank both relevant seen and unseen labels accurately.
Ranked #3 on Multi-label zero-shot learning on Open Images V4
3 code implementations • 30 Mar 2020 • Tal Ridnik, Hussam Lawen, Asaf Noy, Emanuel Ben Baruch, Gilad Sharir, Itamar Friedman
In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.
Ranked #7 on Fine-Grained Image Classification on Oxford 102 Flowers (using extra training data)