no code implementations • 8 Feb 2024 • Roy Ganz, Yair Kittenplon, Aviad Aberdam, Elad Ben Avraham, Oren Nuriel, Shai Mazor, Ron Litman
This integration results in dynamic visual features focusing on relevant image aspects to the posed question.
no code implementations • ICCV 2023 • Roy Ganz, Oren Nuriel, Aviad Aberdam, Yair Kittenplon, Shai Mazor, Ron Litman
Visual Question Answering (VQA) and Image Captioning (CAP), which are among the most popular vision-language tasks, have analogous scene-text versions that require reasoning from the text in the image.
no code implementations • ICCV 2023 • Aviad Aberdam, David Bensaïd, Alona Golts, Roy Ganz, Oren Nuriel, Royee Tichauer, Shai Mazor, Ron Litman
Reading text in real-world scenarios often requires understanding the context surrounding it, especially when dealing with poor-quality text.
no code implementations • 14 Sep 2022 • Sergi Garcia-Bordils, Andrés Mafla, Ali Furkan Biten, Oren Nuriel, Aviad Aberdam, Shai Mazor, Ron Litman, Dimosthenis Karatzas
This paper presents final results of the Out-Of-Vocabulary 2022 (OOV) challenge.
Optical Character Recognition Optical Character Recognition (OCR) +1
2 code implementations • 8 May 2022 • Aviad Aberdam, Roy Ganz, Shai Mazor, Ron Litman
In a novel setup, consistency is enforced on each modality separately.
no code implementations • 18 Mar 2021 • Or Perel, Oron Anschel, Omri Ben-Eliezer, Shai Mazor, Hadar Averbuch-Elor
Nowadays, as cameras are rapidly adopted in our daily routine, images of documents are becoming both abundant and prevalent.
no code implementations • 23 Dec 2020 • Ron Slossberg, Oron Anschel, Amir Markovitz, Ron Litman, Aviad Aberdam, Shahar Tsiper, Shai Mazor, Jon Wu, R. Manmatha
Although the topic of confidence calibration has been an active research area for the last several decades, the case of structured and sequence prediction calibration has been scarcely explored.
2 code implementations • CVPR 2021 • Aviad Aberdam, Ron Litman, Shahar Tsiper, Oron Anschel, Ron Slossberg, Shai Mazor, R. Manmatha, Pietro Perona
We propose a framework for sequence-to-sequence contrastive learning (SeqCLR) of visual representations, which we apply to text recognition.
no code implementations • ECCV 2020 • Amir Markovitz, Inbal Lavi, Or Perel, Shai Mazor, Roee Litman
We present CREASE: Content Aware Rectification using Angle Supervision, the first learned method for document rectification that relies on the document's content, the location of the words and specifically their orientation, as hints to assist in the rectification process.
Optical Character Recognition Optical Character Recognition (OCR)
2 code implementations • CVPR 2020 • Ron Litman, Oron Anschel, Shahar Tsiper, Roee Litman, Shai Mazor, R. Manmatha
The first attention step re-weights visual features from a CNN backbone together with contextual features computed by a BiLSTM layer.
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.
no code implementations • CVPR 2017 • Michael Schober, Amit Adam, Omer Yair, Shai Mazor, Sebastian Nowozin
Operating in this mode the camera essentially forgets all information previously captured - and performs depth inference from scratch for every frame.
no code implementations • 22 Jul 2015 • Amit Adam, Christoph Dann, Omer Yair, Shai Mazor, Sebastian Nowozin
We propose a computational model for shape, illumination and albedo inference in a pulsed time-of-flight (TOF) camera.