no code implementations • 21 Sep 2022 • Khanh Nguyen, Ali Furkan Biten, Andres Mafla, Lluis Gomez, Dimosthenis Karatzas
Particularly, a similar Wikimedia image can be used to illustrate different articles, and the produced caption needs to be adapted to a specific context, therefore allowing us to explore the limits of a model to adjust captions to different contextual information.
no code implementations • 14 Sep 2022 • Emanuele Vivoli, Ali Furkan Biten, Andres Mafla, Dimosthenis Karatzas, Lluis Gomez
In this paper, we present a framework for Multilingual Scene Text Visual Question Answering that deals with new languages in a zero-shot fashion.
1 code implementation • 9 Mar 2022 • Mohamed Ali Souibgui, Sanket Biswas, Andres Mafla, Ali Furkan Biten, Alicia Fornés, Yousri Kessentini, Josep Lladós, Lluis Gomez, Dimosthenis Karatzas
In this paper, we propose a Text-Degradation Invariant Auto Encoder (Text-DIAE), a self-supervised model designed to tackle two tasks, text recognition (handwritten or scene-text) and document image enhancement.
no code implementations • 6 Oct 2021 • Ali Furkan Biten, Andres Mafla, Lluis Gomez, Dimosthenis Karatzas
In this work, we propose two metrics that evaluate the degree of semantic relevance of retrieved items, independently of their annotated binary relevance.
1 code implementation • 21 Sep 2020 • Andres Mafla, Sounak Dey, Ali Furkan Biten, Lluis Gomez, Dimosthenis Karatzas
Scene text instances found in natural images carry explicit semantic information that can provide important cues to solve a wide array of computer vision problems.
2 code implementations • 14 Jan 2020 • Andres Mafla, Sounak Dey, Ali Furkan Biten, Lluis Gomez, Dimosthenis Karatzas
Text contained in an image carries high-level semantics that can be exploited to achieve richer image understanding.
Ranked #1 on Fine-Grained Image Classification on Con-Text
no code implementations • 30 Jun 2019 • Ali Furkan Biten, Rubèn Tito, Andres Mafla, Lluis Gomez, Marçal Rusiñol, Minesh Mathew, C. V. Jawahar, Ernest Valveny, Dimosthenis Karatzas
ST-VQA introduces an important aspect that is not addressed by any Visual Question Answering system up to date, namely the incorporation of scene text to answer questions asked about an image.
3 code implementations • ICCV 2019 • Ali Furkan Biten, Ruben Tito, Andres Mafla, Lluis Gomez, Marçal Rusiñol, Ernest Valveny, C. V. Jawahar, Dimosthenis Karatzas
Current visual question answering datasets do not consider the rich semantic information conveyed by text within an image.