1 code implementation • 16 Jan 2024 • Tal Ridnik, Dedy Kredo, Itamar Friedman
Hence, many of the optimizations and tricks that have been successful in natural language generation may not be effective for code tasks.
1 code implementation • CVPR 2022 • Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman, Avi Ben-Cohen, Nadav Zamir, Asaf Noy, Lihi Zelnik-Manor
We propose to estimate the class distribution using a dedicated temporary model, and we show its improved efficiency over a naive estimation computed using the dataset's partial annotations.
Ranked #1 on Multi-Label Classification on OpenImages-v6
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
5 code implementations • ICCV 2021 • Emanuel Ben-Baruch, Tal Ridnik, Nadav Zamir, Asaf Noy, Itamar Friedman, Matan Protter, Lihi Zelnik-Manor
In this paper, we introduce a novel asymmetric loss ("ASL"), which operates differently on positive and negative samples.
Ranked #4 on Multi-Label Classification on NUS-WIDE
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)
1 code implementation • 19 Feb 2020 • Yonathan Aflalo, Asaf Noy, Ming Lin, Itamar Friedman, Lihi Zelnik
Through this we produce compact architectures with the same FLOPs as EfficientNet-B0 and MobileNetV3 but with higher accuracy, by $1\%$ and $0. 3\%$ respectively on ImageNet, and faster runtime on GPU.
Ranked #3 on Network Pruning on ImageNet
1 code implementation • CVPR 2020 • Amir Markovitz, Gilad Sharir, Itamar Friedman, Lihi Zelnik-Manor, Shai Avidan
We propose a new method for anomaly detection of human actions.
Ranked #7 on Video Anomaly Detection on HR-UBnormal
no code implementations • 15 Oct 2019 • Hussam Lawen, Avi Ben-Cohen, Matan Protter, Itamar Friedman, Lihi Zelnik-Manor
Furthermore, we show the representation power of our ReID network via SotA results on a different task of multi-object tracking.
Ranked #16 on Person Re-Identification on Market-1501 (Rank-1 metric)
2 code implementations • NeurIPS 2019 • Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik-Manor
This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice.
1 code implementation • 8 Apr 2019 • Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik-Manor
In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations.
no code implementations • 20 Jul 2017 • Gilad Sharir, Eddie Smolyansky, Itamar Friedman
We present an approach to semi-supervised video object segmentation, in the context of the DAVIS 2017 challenge.