Prestigious Women Researchers Lecture Series | Petia Radeva: 'How Self-supervised learning can leverage Food Fine-grained recognition'

The advent of Deep Learning (DL) has led to a super-human performance in many tasks such as face and lips recognition or cancer detection in medical images. However, classification is still an open field in case of a large number of classes where usually groups of classes with significant confusion occur (a.k.a. fine-grained recognition). On the other hand, DL is based on very greedy methods needing thousands of images which annotation is a time-consuming and tedious process. Self-supervised learning claims to offer efficient ways to get use of big amounts of non annotated images to make the DL models more robust and accurate. In this talk, we will present our work on self-supervised learning and fine-grained recognition. We will illustrate how self-supervised learning and fine-grained recognition can help solve complex Computer vision problems like food image recognition where food classes have very high variation, there is a big food class similarity and there are huge amounts of unannotated food images.


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 supervised 24 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings, her Google scholar h-index is 53 with more than 11300 cites. She is an 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. Moreover, she was awarded 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).