no code implementations • COLING (TextGraphs) 2022 • David Montero, Javier Martínez, Javier Yebes
Information extraction on documents still remains a challenge, especially when dealing with unstructured documents with complex and variable layouts.
no code implementations • 25 Jan 2024 • Andrei Tomut, Saeed S. Jahromi, Sukhbinder Singh, Faysal Ishtiaq, Cesar Muñoz, Prabdeep Singh Bajaj, Ali Elborady, Gianni Del Bimbo, Mehrazin Alizadeh, David Montero, Pablo Martin-Ramiro, Muhammad Ibrahim, Oussama Tahiri Alaoui, John Malcolm, Samuel Mugel, Roman Orus
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, substantial energy demands, and limitations for on-site deployment.
no code implementations • 29 Jun 2021 • Fadi Boutros, Naser Damer, Jan Niklas Kolf, Kiran Raja, Florian Kirchbuchner, Raghavendra Ramachandra, Arjan Kuijper, Pengcheng Fang, Chao Zhang, Fei Wang, David Montero, Naiara Aginako, Basilio Sierra, Marcos Nieto, Mustafa Ekrem Erakin, Ugur Demir, Hazim Kemal, Ekenel, Asaki Kataoka, Kohei Ichikawa, Shizuma Kubo, Jie Zhang, Mingjie He, Dan Han, Shiguang Shan, Klemen Grm, Vitomir Štruc, Sachith Seneviratne, Nuran Kasthuriarachchi, Sanka Rasnayaka, Pedro C. Neto, Ana F. Sequeira, Joao Ribeiro Pinto, Mohsen Saffari, Jaime S. Cardoso
These teams successfully submitted 18 valid solutions.
no code implementations • 17 May 2021 • J. Javier Yebes, David Montero, Ignacio Arriola
Our research work tackled the challenge of pothole detection from images of real world road scenes.
no code implementations • 20 Apr 2021 • David Montero, Marcos Nieto, Peter Leskovsky, Naiara Aginako
Experimental results show that the proposed approach highly boosts the original model accuracy when dealing with masked faces, while preserving almost the same accuracy on the original non-masked datasets.
no code implementations • 31 Mar 2021 • David Montero, Naiara Aginako, Basilio Sierra, Marcos Nieto
In this work, we address the problem of large-scale online face clustering: given a continuous stream of unknown faces, create a database grouping the incoming faces by their identity.