Diego Ortego Hernández | PhD

About me

In June 2026 I joined the GeoAI team in Xoople as a Lead Research Scientist. I lead the NLP strategy to connect remote sensing imagery with natural language and beyond.

From April 2021 to June 2026 I was part of the Core Models team at NielsenIQ. I led research initiatives related to recommender systems, aiming at extracting value from the product information. These tasks involved research challenges related to extreme multi-label classification, information retrieval, re-ranking strategies, trustable predictions, label noise or long-tailed label distributions. My role had a strong focus on NLP topics, adopting modern LLM technologies. In the team, we were research-oriented so that we aimed at generating value for the company while contributing to the research community. During my time at NielsenIQ, I participated in contributions in several top-tier venues (AAAI, KDD, NAACL and SIGIR), closely collaborating with Dr. Emilio Almazán, Dr. Mario Almagro, Dr. David Jiménez-Cabello and  and Dr. Kunal Dahiya.

From September 2018 to March 2021, I spent a fantastic time working as a Postdoctoral Researcher at Insight Centre for Data Analytics (Dublin City University) as part of a great team led by Dr. Kevin McGuinness. My research was focused on visual representation learning with limited supervision and we published works at CVPR, ICML, WACV, IJCNN, BMVC, ICASSP and ICPR among others.

From 2012 to 2018, I did my undergraduate, master and PhD theses at the Video Processing and Understanding Lab (Universidad Autónoma de Madrid). During that years, I participated in several projects dealing with computer vision tasks under the supervision of Dr. José M. Martínez and Dr. Juan C. San Miguel.

I received my PhD in Computer Science in 2018 at Universidad Autónoma de Madrid (Spain). Before my PhD, I received the M.S. degree in Electrical Engineering in 2013 and MPhil degree in Research and Innovation for Computer Science in 2014.

Pre-prints

Publications

2026

• Diego Ortego, Marlon Rodríguez, Mario Almagro, Kunal Dahiya, David Jiménez and Juan C. SanMiguel, "Large Language Models Meet Extreme Multi-label Classification: Scaling and Multi-modal Framework", in Annual AAAI Conference on Artificial Intelligence (AAAI), 2026  (Accepted). [Paper][Code].

2025

• Kunal Dahiya*, Diego Ortego* and David Jiménez,  "Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss ", in Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL), 2025 (*Equal contribution) [Paper][Code].

• Mario Almagro, Diego Ortego and David Jiménez, "Fine-grained auxiliary learning for real-world product recommendation", in International Congress of the Spanish Society for Natural Language Processing (SEPLN), 2025. [Paper].

2023

• Mario Almagro*, Emilio Almazán*, Diego Ortego, and David Jiménez*, "LEA: Improving Sentence Similarity Robustness to Typos Using Lexical Attention Bias", in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2023.  (*Equal contribution) [Paper].

2022

• Mario Almagro*, David Jiménez*, Diego Ortego*, Emilio Almazán* and Eva Martínez Garcia, "Block–SCL: Blocking Matters for Supervised Contrastive Learning in Product Matching", in International ACM Conference on Research and Development in Information Retrieval Workshop (SIGIR Workshop On eCommerce), 2022.  (*Equal contribution) [Paper]

• Enric Moreu, Kevin McGuinness, Diego Ortego and Noel E. O'Connor,  "Domain Randomization for Object Counting", in Irish Conference on Artificial Intelligence and Cognitive Science (AICS), 2022. [Project page] [Paper]

• Paul Albert, Diego Ortego, Eric Arazo, Noel E. O'Connor and Kevin McGuinness, "Addressing out-of-distribution label noise in webly-labelled data". in Winter Conference on Applications of Computer Vision (WACV), 2022. [Project page] [Paper].

2021

• Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor and Kevin McGuinness, "How Important is Importance Sampling for Deep Budgeted Training?", in British Machine Vision Conference (BMVC), 2021. [Project page] [Paper]

• Paul Albert, Diego Ortego, Eric Arazo, Noel E. O'Connor and Kevin McGuinness, "ReLaB: Reliable Label Bootstrapping for Semi-Supervised Learning", in International Joint Conference on Neural Networks (IJCNN), 2021. [Project page] [Paper].

• Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor and Kevin McGuinness, "Multi-Objective Interpolation Training for Robustness to Label Noise", in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021.  [Project page] [Paper]

• Eduardo Fonseca*, Diego Ortego*, Kevin McGuinness, Noel E. O'Connor and Xavier Serra, "Unsupervised Contrastive Learning of Sound Event Representations", in IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021. (*Equal contribution) [Project page] [Paper].

2020

• Eric Arazo, Diego Ortego, Paul Albert, Noel E. O'Connor and Kevin McGuinness, "Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning", in International Joint Conference on Neural Networks (IJCNN), 2020. [Project page] [Paper]

• Diego Ortego, Eric Arazo, Paul Albert, Noel E. O'Connor and Kevin McGuinness, "Towards Robust Learning with Different Label Noise Distributions", in International Conference on Pattern Recognition (ICPR), 2020. Oral presentation (acceptance rate for oral: 5.2%).  [Project page] [Paper]

2019

• Eric Arazo*, Diego Ortego*, Paul Albert, Noel E. O'Connor and Kevin McGuinness, "Unsupervised Label Noise Modeling and Loss Correction", in International Conference on Machine Learning (ICML), 2019. Acceptance rate: 22.6% (*Equal contribution) [Project page] [Paper]

• Diego Ortego, Juan C. SanMiguel, José M. Martínez, "Hierarchical Improvement of Foreground Segmentation Masks in Background Subtraction", in IEEE  Transactions on Circuits and Systems for Video Technology, vol. 29, no. 6, pp. 1645-1658, 2019. [Paper]

• Diego Ortego, Kevin McGuinness, Juan C. SanMiguel, Eric Arazo, José M. Martínez and Noel E. O'Connor, "On guiding video object segmentation", in International Conference on Content-Based Multimedia Indexing (CBMI), 2019. [Paper]

Before 2019

• Elena Luna, Juan C. SanMiguel, Diego Ortego and José M. Martínez, "Abandoned Object Detection in Video-Surveillance: Survey and Comparison", in Sensors, vol. 18, no. 12, art. 4290, 2018. [Paper]

• Pengfei Zhu et al., "VisDrone-VDT2018: The Vision Meets Drone Video Detection and Tracking Challenge Results", in European Conference on Computer Vision Workshops (ECCVW), 2018.

• Elena Luna, Juan C. SanMiguel, Diego Ortego, and José M. Martı́nez, "Multi-target Tracking from Unmanned Aerial Vehicles", Late-breaking work at  Iberoamerican Congress on Pattern Recognition (CIARP), 2018. [Paper]

• Diego Ortego, Juan C. SanMiguel, José M. Martínez, "Stand-alone quality estimation of background subtraction algorithms", in Computer Vision and Image Understanding, vol. 162, pp. 87-102, 2017. [Paper]

• Diego Ortego, Juan C. SanMiguel, José M. Martínez, "Rejection based multipath reconstruction for background estimation in video sequences with stationary objects", in Computer Vision and Image Understanding, vol. 147, pp. 23-37, 2016. [Project page] [Paper]

• Diego Ortego, Juan C. SanMiguel, José M. Martínez, "Rejection based multipath reconstruction for background estimation in SBMnet 2016 dataset," in Proceedings of International Conference on Pattern Recognition  Workshops (ICPRW), pp. 114-119, Cancún, 2016. [Project page]

• Diego Ortego, Juan C. SanMiguel, José M. Martíenz, "Long-Term Stationary Object Detection Based on Spatio-Temporal Change Detection", in IEEE Signal Processing Letters, vol. 22, no. 12, pp. 2368-2372, 2015.

• Diego Ortego and Juan C. SanMiguel, "Multi-feature stationary foreground detection for crowded video-surveillance", in IEEE International Conference on Image Processing (ICIP),  2014. [Project page]

• Diego Ortego and Juan C. SanMiguel, "Stationary foreground detection for video-surveillance based on foreground and motion history images," in IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2013.

Talks

• March 2026. Deep Learning for Audio and Video Signal Processing Master. "Deep Learning for NLP: From zero to Large Language Models" talk. Universidad Autónoma de Madrid  (Spain).

• January 2026. Annual AAAI Conference on Artificial Intelligence 2026 (AAAI'26). Singapore. Paper presentation.

• May 2025. Annual Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics (NAACL). Albuquerque, NM (USA). Paper presentation. 

• April 2025. Deep Learning for Audio and Video Signal Processing Master. "Deep Learning for NLP: From zero to Large Language Models" talk. Universidad Autónoma de Madrid  (Spain).

• March 2024. Deep Learning for Audio and Video Signal Processing Master. "Deep Learning for NLP: From zero to Large Language Models" talk. Universidad Autónoma de Madrid  (Spain).

• April 2023. Deep Learning for Computer Vision Workshop (IPCV master). Universidad Autónoma de Madrid (Spain).

• March 2023. Deep Learning for Audio and Video Signal Processing Master. "Deep Learning for NLP: From zero to Large Language Models" talk. Universidad Autónoma de Madrid  (Spain).

• July 2022. International ACM SIGIR Conference on Research and Development in Information Retrieval. Paper presentation @ SIGIR eCom'22. Madrid (Spain).

• April 2022. Deep Learning for Computer Vision Workshop (IPCV master). Universidad Autónoma de Madrid (Spain).

• March 2022. Deep Learning for Audio and Video Signal Processing Master. "Deep Learning for NLP" talk. Universidad Autónoma de Madrid  (Spain).

• June 2021. Conference on Computer Vision and Pattern Recognition (CVPR). Paper presentation (remote talk).

• April 2021. Deep Learning for Computer Vision Workshop (IPCV master). Remote talk for Universidad Autónoma de Madrid (Spain).

• January 2021. International Conference on Pattern Recognition. Paper presentation (remote talk).

• April 2020. Deep Learning for Computer Vision Workshop (IPCV master). Remote talk for Universidad Autónoma de Madrid (Spain).

• October 2019. ML-labs bootcamp. Location: Dublin City University (Ireland).

• September 2019. International Conference on Content-Based Multimedia Indexing. Location: Dublin City University (Ireland).

• June 2019. International Conference on Machine Learning. Location: Long Beach, CA (USA). Paper presentation.

• March 2019. Deep Learning for Computer Vision Workshop (IPCV master). Location: Universidad Autónoma de Madrid (Spain).

• December 2016. International Conference on Pattern Recognition. Location: Cancun (Mexico). Paper presentation.

• October 2014. International Conference on Image Processing. Location: Paris (France). Paper presentation.

Awards

• Prize "AIRBUS DEFENCE AND SPACE to the best Degree dissertation thesis in Secure Communications and Cyber-security". Year: 2013. Given by: Colegio Oficial de Ingenieros de Telecomunicación (COIT) and Asociación Española de Ingenieros de Telecomunicación (AEIT).

• Research Mobility Award 2020 to collaborate with the Music Technology Group (MTG) from Pompeu Fabra University (Barcelona, Spain). Topic: Label noise in image classification and sound event classification.  Year: 2020. Given by: Young European Research University Network (YERUN).