Speakers 2024

Keynote Speaker Ⅰ

Prof. Petia Radeva

IAPR Fellow

Universitat de Barcelona, Spain & Barcelona Supercomputing Center, Spain

Biography: Prof. Petia Radeva is a Full professor at the Universitat de Barcelona (UB), Head of the Consolidated Research Group “Artificial Intelligence and Biomedical Applications (AIBA)” at the University of Barcelona. Her main interests are in Machine/Deep learning and Computer Vision and their applications to health. Specific topics of interest: data-centric deep learning, uncertainty modeling, self-supervised learning, continual learning, learning with noisy labeling, multi-modal learning, NeRF, food recognition, food ontology, etc. She was PI of UB in 7 European, 3 international and more than 25 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). She is an Associate editor in Chief of Pattern Recognition journal (Q1, IP=8.0). She is a Research Manager of the State Agency of Research (Agencia Estatal de Investigación, AEI) of the Ministry of Science and Innovation of Spain. Petia Radeva belongs to the top 2% of the World ranking of scientists with the major impact in the field of TIC according to the citations indicators of the popular ranking of Stanford. Also, she was selected in the first 6% of the ranking of Spanish and foreign most cited female researchers from any field according to the Ranking of CSIC: https://lnkd.in/djx2Yz5p. Moreover, she was awarded the prestigous “Narcis Monturiol” medal in 2024, IAPR Fellow since 2015, ICREA Academia’2015 and ICREA Academia’2022 assigned to the 30 best scientists in Catalonia for her scientific merits, received several international and national awards (“Aurora Pons Porrata” of CIARP, Prize “Antonio Caparrós” for the best technology transfer at UB, etc). She supervised 24 PhD students and published more than 100 SCI journal publications and in total, >400 international chapters and proceedings, her Google scholar h-index is 54 with >11900 cites.

Speech Title: Data-centric Food Computing

Abstract: Deep Learning (DL) has made remarkable progress, achieving super-human performance. However, when it comes to classifying a complex domain as food recognition, there is still much room for improvement. Additionally, DL relies on greedy methods that require thousands of annotated images, which can be a time-consuming and tedious process.

To address these issues, we will discuss several data-centric approaches that help to the problem, especially how self-supervised learning offers an efficient way to leverage a large amount of non-annotated images and to make DL models more robust and accurate. Moreover, we will present how a new combination of self-supervised, and prompt learning algorithms can help to the fine-grained food recognition.

We applied our algorithms successfully in different European and Spanish projects to provide automatic monitoring of food intake and help people eat better. This tool is essential to patient with kidney transplants, elderly, diabetic and cardiovascular patients as well as all people that want to have a better lifestyle and wellbeing.