Exploring Food Detection Using CNNs. - Eduardo Aguilar 001; Marc Bolaños; Petia Radeva (EUROCAST (2), 2017)

Food Recognition Using Fusion of Classifiers Based on CNNs. - Eduardo Aguilar 001; Marc Bolaños; Petia Radeva (ICIAP (2), 2017)

Multiple Wavelet Pooling for CNNs. - Aina Ferrà; Eduardo Aguilar 001; Petia Radeva (ECCV Workshops (4), 2018)

Grab, Pay, and Eat: Semantic Food Detection for Smart Restaurants. - Eduardo Aguilar 001; Beatriz Remeseiro; Marc Bolaños; Petia Radeva (IEEE Trans. Multim., 2018)

Class-Conditional Data Augmentation Applied to Image Classification. - Eduardo Aguilar 001; Petia Radeva (CAIP (2), 2019)

Food Recognition by Integrating Local and Flat Classifiers. - Eduardo Aguilar 001; Petia Radeva (IbPRIA (1), 2019)

Regularized uncertainty-based multi-task learning model for food analysis. - Eduardo Aguilar 001; Marc Bolaños; Petia Radeva (J. Vis. Commun. Image Represent., 2019)

Uncertainty Modeling and Deep Learning Applied to Food Image Analysis. - Eduardo Aguilar 001; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva (BIOSTEC, 2020)

Uncertainty-Aware Data Augmentation for Food Recognition. - Eduardo Aguilar 001; Bhalaji Nagarajan; Rupali Khatun; Marc Bolaños; Petia Radeva (ICPR, 2020)

S2ML-TL Framework for Multi-label Food Recognition. - Bhalaji Nagarajan; Eduardo Aguilar 001; Petia Radeva (ICPR Workshops (5), 2020)

Uncertainty-aware integration of local and flat classifiers for food recognition. - Eduardo Aguilar 001; Petia Radeva (Pattern Recognit. Lett., 2020)

Uncertainty-aware selecting for an ensemble of deep food recognition models. - Eduardo Aguilar 001; Bhalaji Nagarajan; Petia Radeva (Comput. Biol. Medicine, 2022)

Bayesian deep learning for semantic segmentation of food images. - Eduardo Aguilar 001; Bhalaji Nagarajan; Beatriz Remeseiro; Petia Radeva (Comput. Electr. Eng., 2022)

Continual Evidential Deep Learning for Out-of-Distribution Detection. - Eduardo Aguilar 001; Bogdan Raducanu; Petia Radeva; Joost van de Weijer 001 (ICCV (Workshops), 2023)

Deep ensemble-based hard sample mining for food recognition. - Bhalaji Nagarajan; Marc Bolaños; Eduardo Aguilar 001; Petia Radeva (J. Vis. Commun. Image Represent., 2023)

An Uncertainty-Driven ScaledYOLOv4 for Open-Pit Mining Helmet Detection. - Roger Calle; Eduardo Aguilar 001 (CIARP (2), 2024)

Towards a Lightweight CNN for Semantic Food Segmentation. - Bastián Muñoz; Beatriz Remeseiro; Eduardo Aguilar 001 (CIARP (1), 2024)

Multi-label Out-of-Distribution Detection via Evidential Learning. - Eduardo Aguilar 001; Bogdan Raducanu; Petia Radeva (ECCV Workshops (17), 2024)

Ontology-Driven Deep Learning Model for Multitask Visual Food Analysis. - Daniel Ponte; Eduardo Aguilar 001; Mireia Ribera; Petia Radeva (VISIGRAPP (2): VISAPP, 2024)

Bayesian DivideMix++ for Enhanced Learning with Noisy Labels. - Bhalaji Nagarajan; Ricardo Marques; Eduardo Aguilar 001; Petia Radeva (Neural Networks, 2024)

Robust Deep Neural Network for Learning in Noisy Multi-Label Food Images. - Roberto Morales; Angela Martinez-Arroyo; Eduardo Aguilar 001 (Sensors, 2024)

Slot Attention-based Feature Filtering for Few-Shot Learning. - Javier Ródenas Cumplido; Eduardo Aguilar 001; Petia Radeva (CVPR Workshops, 2025)

Stochastic-based Patch Filtering for Few-Shot Learning. - Javier Ródenas Cumplido; Eduardo Aguilar 001; Petia Radeva (CVPR Workshops, 2025)

Uncertainty-Driven Past-Sample Selection for Replay-Based Continual Learning. - Anxo-Lois Pereira; Eduardo Aguilar 001; Petia Radeva (VISIGRAPP (3): VISAPP, 2025)

CEDL+: Exploiting evidential deep learning for continual out-of-distribution detection. - Eduardo Aguilar 001; Bogdan Raducanu; Petia Radeva; Joost van de Weijer 001 (Expert Syst. Appl., 2025)

Multi-task visual food recognition by integrating an ontology supported with LLM. - Daniel Ponte; Eduardo Aguilar 001; Mireia Ribera; Petia Radeva (J. Vis. Commun. Image Represent., 2025)

Inter-separability and intra-concentration to enhance stochastic neural network adversarial robustness. - Omar Dardour; Eduardo Aguilar 001; Petia Radeva; Mourad Zaied (Pattern Recognit. Lett., 2025)