Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Tito, Rubén"'
Autor:
Tobaben, Marlon, Souibgui, Mohamed Ali, Tito, Rubèn, Nguyen, Khanh, Kerkouche, Raouf, Jung, Kangsoo, Jälkö, Joonas, Kang, Lei, Barsky, Andrey, d'Andecy, Vincent Poulain, Joseph, Aurélie, Muhamed, Aashiq, Kuo, Kevin, Smith, Virginia, Yamasaki, Yusuke, Fukami, Takumi, Niwa, Kenta, Tyou, Iifan, Ishii, Hiro, Yokota, Rio, N, Ragul, Kutum, Rintu, Llados, Josep, Valveny, Ernest, Honkela, Antti, Fritz, Mario, Karatzas, Dimosthenis
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a federated setting for a real-life use case: invoice processing. The compet
Externí odkaz:
http://arxiv.org/abs/2411.03730
Documents are 2-dimensional carriers of written communication, and as such their interpretation requires a multi-modal approach where textual and visual information are efficiently combined. Document Visual Question Answering (Document VQA), due to t
Externí odkaz:
http://arxiv.org/abs/2404.19024
Autor:
Tito, Rubèn, Nguyen, Khanh, Tobaben, Marlon, Kerkouche, Raouf, Souibgui, Mohamed Ali, Jung, Kangsoo, Jälkö, Joonas, D'Andecy, Vincent Poulain, Joseph, Aurelie, Kang, Lei, Valveny, Ernest, Honkela, Antti, Fritz, Mario, Karatzas, Dimosthenis
Document Visual Question Answering (DocVQA) has quickly grown into a central task of document understanding. But despite the fact that documents contain sensitive or copyrighted information, none of the current DocVQA methods offers strong privacy gu
Externí odkaz:
http://arxiv.org/abs/2312.10108
Autor:
Van Landeghem, Jordy, Tito, Rubén, Borchmann, Łukasz, Pietruszka, Michał, Józiak, Paweł, Powalski, Rafał, Jurkiewicz, Dawid, Coustaty, Mickaël, Ackaert, Bertrand, Valveny, Ernest, Blaschko, Matthew, Moens, Sien, Stanisławek, Tomasz
We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research p
Externí odkaz:
http://arxiv.org/abs/2305.08455
Document Visual Question Answering (DocVQA) refers to the task of answering questions from document images. Existing work on DocVQA only considers single-page documents. However, in real scenarios documents are mostly composed of multiple pages that
Externí odkaz:
http://arxiv.org/abs/2212.05935
Pretraining has proven successful in Document Intelligence tasks where deluge of documents are used to pretrain the models only later to be finetuned on downstream tasks. One of the problems of the pretraining approaches is the inconsistent usage of
Externí odkaz:
http://arxiv.org/abs/2202.12985
In this report we present results of the ICDAR 2021 edition of the Document Visual Question Challenges. This edition complements the previous tasks on Single Document VQA and Document Collection VQA with a newly introduced on Infographics VQA. Infogr
Externí odkaz:
http://arxiv.org/abs/2111.05547
Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purchase invoices), that provide context useful for their interpretation. T
Externí odkaz:
http://arxiv.org/abs/2104.14336
Autor:
Mathew, Minesh, Bagal, Viraj, Tito, Rubèn Pérez, Karatzas, Dimosthenis, Valveny, Ernest, Jawahar, C. V
Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements. In this work, we explore the automatic understanding of infographic images by using Visual Question Answering te
Externí odkaz:
http://arxiv.org/abs/2104.12756
This paper presents results of Document Visual Question Answering Challenge organized as part of "Text and Documents in the Deep Learning Era" workshop, in CVPR 2020. The challenge introduces a new problem - Visual Question Answering on document imag
Externí odkaz:
http://arxiv.org/abs/2008.08899