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pro vyhledávání: '"Fogel, Sharon"'
Autor:
Abramovich, Ofir, Nayman, Niv, Fogel, Sharon, Lavi, Inbal, Litman, Ron, Tsiper, Shahar, Tichauer, Royee, Appalaraju, Srikar, Mazor, Shai, Manmatha, R.
In recent years, notable advancements have been made in the domain of visual document understanding, with the prevailing architecture comprising a cascade of vision and language models. The text component can either be extracted explicitly with the u
Externí odkaz:
http://arxiv.org/abs/2407.12594
Autor:
Blau, Tsachi, Fogel, Sharon, Ronen, Roi, Golts, Alona, Ganz, Roy, Avraham, Elad Ben, Aberdam, Aviad, Tsiper, Shahar, Litman, Ron
The increasing use of transformer-based large language models brings forward the challenge of processing long sequences. In document visual question answering (DocVQA), leading methods focus on the single-page setting, while documents can span hundre
Externí odkaz:
http://arxiv.org/abs/2401.03411
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. Existing methods usually have a distinct separation between the detection and r
Externí odkaz:
http://arxiv.org/abs/2202.05508
Leveraging the characteristics of convolutional layers, neural networks are extremely effective for pattern recognition tasks. However in some cases, their decisions are based on unintended information leading to high performance on standard benchmar
Externí odkaz:
http://arxiv.org/abs/2105.03906
Non-visual imaging sensors are widely used in the industry for different purposes. Those sensors are more expensive than visual (RGB) sensors, and usually produce images with lower resolution. To this end, Cross-Modality Super-Resolution methods were
Externí odkaz:
http://arxiv.org/abs/2004.09965
Optical character recognition (OCR) systems performance have improved significantly in the deep learning era. This is especially true for handwritten text recognition (HTR), where each author has a unique style, unlike printed text, where the variati
Externí odkaz:
http://arxiv.org/abs/2003.10557
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. For example, decorative text that specifies where the image was taken, repeatedly appea
Externí odkaz:
http://arxiv.org/abs/1904.02756
We present a novel approach to learning a point-wise, meaningful embedding for point-clouds in an unsupervised manner, through the use of neural-networks. The domain of point-cloud processing via neural-networks is rapidly evolving, with novel archit
Externí odkaz:
http://arxiv.org/abs/1901.04544
Recently, there has been increasing interest to leverage the competence of neural networks to analyze data. In particular, new clustering methods that employ deep embeddings have been presented. In this paper, we depart from centroid-based models and
Externí odkaz:
http://arxiv.org/abs/1803.08457
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