no code implementations • 1 May 2024 • Oshri Naparstek, Roi Pony, Inbar Shapira, Foad Abo Dahood, Ophir Azulai, Yevgeny Yaroker, Nadav Rubinstein, Maksym Lysak, Peter Staar, Ahmed Nassar, Nikolaos Livathinos, Christoph Auer, Elad Amrani, Idan Friedman, Orit Prince, Yevgeny Burshtein, Adi Raz Goldfarb, Udi Barzelay
In recent years, the challenge of extracting information from business documents has emerged as a critical task, finding applications across numerous domains.
1 code implementation • 17 May 2022 • Daniel Rotman, Yevgeny Yaroker, Elad Amrani, Udi Barzelay, Rami Ben-Ari
Video scene detection is the task of dividing videos into temporal semantic chapters.
1 code implementation • CVPR 2022 • Sivan Harary, Eli Schwartz, Assaf Arbelle, Peter Staar, Shady Abu-Hussein, Elad Amrani, Roei Herzig, Amit Alfassy, Raja Giryes, Hilde Kuehne, Dina Katabi, Kate Saenko, Rogerio Feris, Leonid Karlinsky
The ability to generalize learned representations across significantly different visual domains, such as between real photos, clipart, paintings, and sketches, is a fundamental capacity of the human visual system.
2 code implementations • 19 Mar 2021 • Elad Amrani, Leonid Karlinsky, Alex Bronstein
To guarantee non-degenerate solutions (i. e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entropy loss that has a uniform prior asserted on the predicted labels.
Ranked #3 on Unsupervised Image Classification on ImageNet
1 code implementation • 6 Mar 2020 • Elad Amrani, Rami Ben-Ari, Daniel Rotman, Alex Bronstein
One of the key factors of enabling machine learning models to comprehend and solve real-world tasks is to leverage multimodal data.
Ranked #3 on Visual Question Answering on MSRVTT-QA (Accuracy metric)
1 code implementation • 27 May 2019 • Elad Amrani, Rami Ben-Ari, Tal Hakim, Alex Bronstein
In this work, we propose to exploit the natural correlation in narrations and the visual presence of objects in video, to learn an object detector and retrieval without any manual labeling involved.