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Ernest Valveny

3 accepted papers

2025

DocVXQA: Context-Aware Visual Explanations for Document Question Answering

Mohamed Ali Souibgui, Changkyu Choi, Andrey Barsky, Kangsoo Jung, Ernest Valveny, Dimosthenis Karatzas
ICML 2025poster

We propose **DocVXQA**, a novel framework for visually self-explainable document question answering, where the goal is not only to produce accurate answers to questions but also to learn visual heatmaps that highlight critical regions, offering interpretable justifications for the model decision. To…

Cited by 7Source ↗PDF ↗code ↗Scholar ↗
2023

Document Understanding Dataset and Evaluation (DUDE)

Jordy Van Landeghem, Rubèn Tito, Łukasz Borchmann, Michał Pietruszka, Pawel Joziak, Rafal Powalski +7
ICCV 2023poster

We call on the Document AI (DocAI) community to re-evaluate current methodologies and embrace the challenge of creating more practically-oriented benchmarks. Document Understanding Dataset and Evaluation (DUDE) seeks to remediate the halted research progress in understanding visually-rich documents…

Cited by 67PDF ↗code ↗Scholar ↗
2019

Scene Text Visual Question Answering

Ali Furkan Biten, Ruben Tito, Andres Mafla, Lluis Gomez, Marcal Rusinol, Ernest Valveny +2
ICCV 2019poster

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 to highlight the importance of exploiting high-level semantic information present in images as textual cues in the Visu…

Cited by 417PDF ↗Scholar ↗