AIDA-UPM at SemEval-2022 Task 5: Exploring Multimodal Late Information Fusion for Multimedia Automatic Misogyny Identification

1 code implementation • SemEval (NAACL) 2022 • Álvaro Huertas-García, Helena Liz, Guillermo Villar-Rodríguez, Alejandro Martín, Javier Huertas-Tato, David Camacho

The main contribution of this paper is the exploration of different late fusion methods to boost the performance of the combination based on the Transformer-based model and Convolutional Neural Networks (CNN) for text and image, respectively.

Meme Classification

Deep learning for understanding multilabel imbalanced Chest X-ray datasets

no code implementations • 28 Jul 2022 • Helena Liz, Javier Huertas-Tato, Manuel Sánchez-Montañés, Javier Del Ser, David Camacho

To apply these algorithms in different fields and test how the methodology works, we need to use eXplainable AI techniques.

Ensembles of Convolutional Neural Networks models for pediatric pneumonia diagnosis

no code implementations • 29 Sep 2020 • Helena Liz, Manuel Sánchez-Montañés, Alfredo Tagarro, Sara Domínguez-Rodríguez, Ron Dagan, David Camacho

However, the usability of these systems is limited in medicine due to the lack of interpretability, because of these models cannot be used to generate an understandable explanation (from a human-based perspective), about how they have reached those results.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +1