Petia I. Radeva

Full professor in the Department of Mathematics and Computer Science at the Universtiy of Barcelona, Senior Researcher in CVC, Head of “Artificial Intelligence and Bio-Medical Applications at UB”

Profile summary

Prof. Petia Radeva is a Full professor at the Universitat de Barcelona (UB), PI of the Consolidated Research Group “Artificial Intelligence and Bio-Medical Applications” at the University of Barcelona (AIBA) at UB (www.ub.edu/cvub) and Senior researcher in Computer Vision Center (www.cvc.uab.es). She was PI of UB in 4 European, 3 international and more than 20 national projects devoted to applying Computer Vision and Machine learning for real problems like food intake monitoring (e.g. for patients with kidney transplants and for older people). Petia Radeva is a REA-FET-OPEN vice-chair since 2015 on, and international mentor in the Wild Cards EIT program since 2017.

She is an Associate editor of Pattern Recognition journal (Q1) and International Journal of Visual Communication and Image Representation (Q2).

Petia Radeva has been awarded IAPR Fellow since 2015, ICREA Academia assigned to the 30 best scientists in Catalonia for her scientific merits since 2014, received several international awards (“Aurora Pons Porrata” of CIARP, Prize “Antonio Caparrós” for the best technology transfer of UB, etc).

She supervised 22 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings, her Google scholar h-index is 44 with more than 7600 cites and WOS h-index: 79.

Latest publications

Petia I. Radeva has 735 publications on Google Scholar.
Only the last 20 publications are shown here. Access Petia I. Radeva Google Scholar Profile

Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation

2026 | Imanol G Estepa, Jesús M Rodríguez-de-Vera, Bhalaji Nagarajan, Petia Radeva | arXiv preprint arXiv:2605.25012, 2026

Petia I. Radeva

Profile summary

Latest publications

Learning from Semantic Dictionaries: Discriminative Codebook Contrastive Learning for Unified Visual Representation and Generation

Physically Informed 3D Food Reconstruction: Methods and Results

VolE: A point-cloud framework for food 3D reconstruction and volume estimation

BenchSeg: A Large-Scale Dataset and Benchmark for Multi-View Food Video Segmentation

Anatomically guided latent diffusion for high-resolution 3D chest CT synthesis

Computer Vision, Imaging and Computer Graphics Theory and Applications: 19th International Joint Conference, VISIGRAPP 2024, Rome, Italy, February 27–29, 2024, Revised Selected Papers

Conjuring Positive Pairs for Efficient Unification of Representation Learning and Image Synthesis

Efficacy of a mobile health–based behavioral treatment for lifestyle modification in type 2 diabetes self-management: greenhabit randomized controlled trial

A dynamic station-line centrality for identifying critical stations in bus-metro networks

Never compromise to vulnerabilities: A comprehensive survey on ai governance

FoodMem: Near real-time and precise food video segmentation

Calibration and Uncertainty for multiRater Volume Assessment in multiorgan Segmentation (CURVAS) challenge results

Precision at scale: Domain-specific datasets on-demand

Explainable AI model reveals disease-related mechanisms in single-cell RNA-seq data

Representation discrepancy bridging method for remote sensing image-text retrieval

VolTex: food volume estimation using text-guided segmentation and neural surface reconstruction

Multi-task visual food recognition by integrating an ontology supported with LLM

Progress in applications of self-supervised learning to computer vision in agriculture: A systematic review

CEDL+: Exploiting evidential deep learning for continual out-of-distribution detection

Advancing Image-Based Grapevine Variety Classification with a New Benchmark and Evaluation of Masked Autoencoders

Research lines

Deep learning for food computing