22 citations · 41 across the 10 of their papers we have counts for
13 papers
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA
Marlon Tobaben, Mohamed Ali Souibgui, Rubèn Tito +24
The Privacy Preserving Federated Learning Document VQA (PFL-DocVQA) competition challenged the community to develop provably private and communication-efficient solutions in a fede…
Multi-Page Document Visual Question Answering using Self-Attention Scoring Mechanism
Lei Kang, Rubèn Tito, Ernest Valveny +1
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…
Privacy-Aware Document Visual Question Answering
Rubèn Tito, Khanh Nguyen, Marlon Tobaben +11
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 in…
Document Understanding Dataset and Evaluation (DUDE)
Jordy Van Landeghem, Rubén Tito, Łukasz Borchmann +10
We call on the Document AI (DocAI) community to reevaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding…
Hierarchical multimodal transformers for Multi-Page DocVQA
Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny
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, i…
OCR-IDL: OCR Annotations for Industry Document Library Dataset
Ali Furkan Biten, Rubèn Tito, Lluis Gomez +2
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 th…
ICDAR 2021 Competition on Document VisualQuestion Answering
Rubèn Tito, Minesh Mathew, C. V. Jawahar +2
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 Docum…
Document Collection Visual Question Answering
Rubèn Tito, Dimosthenis Karatzas, Ernest Valveny
Current tasks and methods in Document Understanding aims to process documents as single elements. However, documents are usually organized in collections (historical records, purch…
InfographicVQA
Minesh Mathew, Viraj Bagal, Rubèn Pérez Tito +3
Infographics are documents designed to effectively communicate information using a combination of textual, graphical and visual elements. In this work, we explore the automatic und…
Document Visual Question Answering Challenge 2020
Minesh Mathew, Ruben Tito, Dimosthenis Karatzas +2
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…
Multimodal grid features and cell pointers for Scene Text Visual Question Answering
Lluís Gómez, Ali Furkan Biten, Rubèn Tito +4
This paper presents a new model for the task of scene text visual question answering, in which questions about a given image can only be answered by reading and understanding scene…
ICDAR 2019 Competition on Scene Text Visual Question Answering
Ali Furkan Biten, Rubèn Tito, Andres Mafla +6
This paper presents final results of ICDAR 2019 Scene Text Visual Question Answering competition (ST-VQA). ST-VQA introduces an important aspect that is not addressed by any Visual…
Scene Text Visual Question Answering
Ali Furkan Biten, Ruben Tito, Andres Mafla +5
Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image. In this work, we present a new dataset, ST-VQA, that aims…