no code implementations • 28 May 2024 • Issar Tzachor, Boaz Lerner, Matan Levy, Michael Green, Tal Berkovitz Shalev, Gavriel Habib, Dvir Samuel, Noam Korngut Zailer, Or Shimshi, Nir Darshan, Rami Ben-Ari
In this paper, we propose a simple yet powerful approach to better exploit the potential of a foundation model for VPR.
Ranked #1 on Visual Place Recognition on Eynsham
no code implementations • 28 May 2024 • Dvir Samuel, Rami Ben-Ari, Matan Levy, Nir Darshan, Gal Chechik
Personalized retrieval and segmentation aim to locate specific instances within a dataset based on an input image and a short description of the reference instance.
1 code implementation • 19 Dec 2023 • Dvir Samuel, Barak Meiri, Nir Darshan, Shai Avidan, Gal Chechik, Rami Ben-Ari
Diffusion inversion is the problem of taking an image and a text prompt that describes it and finding a noise latent that would generate the image.
1 code implementation • NeurIPS 2023 • Dvir Samuel, Rami Ben-Ari, Nir Darshan, Haggai Maron, Gal Chechik
Text-to-image diffusion models show great potential in synthesizing a large variety of concepts in new compositions and scenarios.
1 code implementation • 27 Apr 2023 • Dvir Samuel, Rami Ben-Ari, Simon Raviv, Nir Darshan, Gal Chechik
We show that their limitation is partly due to the long-tail nature of their training data: web-crawled data sets are strongly unbalanced, causing models to under-represent concepts from the tail of the distribution.
no code implementations • ICCV 2021 • Dvir Samuel, Gal Chechik
The new robustness loss can be combined with various classifier balancing techniques and can be applied to representations at several layers of the deep model.
Ranked #17 on Long-tail Learning on CIFAR-100-LT (ρ=10)
1 code implementation • 5 Apr 2020 • Dvir Samuel, Yuval Atzmon, Gal Chechik
Real-world data is predominantly unbalanced and long-tailed, but deep models struggle to recognize rare classes in the presence of frequent classes.
Ranked #1 on Long-tail learning with class descriptors on CUB-LT