Search Results for author: Sharon Fogel

Found 7 papers, 4 papers with code

GRAM: Global Reasoning for Multi-Page VQA

no code implementations7 Jan 2024 Tsachi Blau, Sharon Fogel, Roi Ronen, Alona Golts, Roy Ganz, Elad Ben Avraham, Aviad Aberdam, Shahar Tsiper, Ron Litman

The increasing use of transformer-based large language models brings forward the challenge of processing long sequences.

Question Answering Visual Question Answering

Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer

no code implementations CVPR 2022 Yair Kittenplon, Inbal Lavi, Sharon Fogel, Yarin Bar, R. Manmatha, Pietro Perona

Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components.

Text Detection Text Spotting

TextAdaIN: Paying Attention to Shortcut Learning in Text Recognizers

1 code implementation9 May 2021 Oren Nuriel, Sharon Fogel, Ron Litman

However in some cases, their decisions are based on unintended information leading to high performance on standard benchmarks but also to a lack of generalization to challenging testing conditions and unintuitive failures.

Handwritten Text Recognition Scene Text Recognition

Single Pair Cross-Modality Super Resolution

no code implementations CVPR 2021 Guy Shacht, Sharon Fogel, Dov Danon, Daniel Cohen-Or, Ilya Leizerson

The network is trained on the two input images only, learns their internal statistics and correlations, and applies them to up-sample the target modality.

Super-Resolution

ScrabbleGAN: Semi-Supervised Varying Length Handwritten Text Generation

3 code implementations CVPR 2020 Sharon Fogel, Hadar Averbuch-Elor, Sarel Cohen, Shai Mazor, Roee Litman

This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variation is smaller by design.

Domain Adaptation Handwriting generation +4

Blind Visual Motif Removal from a Single Image

1 code implementation CVPR 2019 Amir Hertz, Sharon Fogel, Rana Hanocka, Raja Giryes, Daniel Cohen-Or

Many images shared over the web include overlaid objects, or visual motifs, such as text, symbols or drawings, which add a description or decoration to the image.

Clustering-driven Deep Embedding with Pairwise Constraints

1 code implementation22 Mar 2018 Sharon Fogel, Hadar Averbuch-Elor, Jacov Goldberger, Daniel Cohen-Or

In this paper, we depart from centroid-based models and suggest a new framework, called Clustering-driven deep embedding with PAirwise Constraints (CPAC), for non-parametric clustering using a neural network.

Clustering

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