Uncertainty Modeling within an End-to-end Framework for Food Image Analysis
Petia Radeva, Mathematics and Computer Science, Universitat de Barcelona, Spain
3D Scene Understanding from a Single Image
Vincent Lepetit, École des Ponts ParisTech, France
Uncertainty Modeling within an End-to-end Framework for Food Image Analysis
Petia Radeva
Mathematics and Computer Science, Universitat de Barcelona
Spain
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Brief Bio
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 is an Associate editor in Chief of Pattern Recognition journal and International Journal of Visual Communication and Image Representation. 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. She supervised 24 PhD students and published more than 100 SCI journal publications and 250 international chapters and proceedings. 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. 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”, Prize “Antonio Caparrós” ).
Abstract
Recently, computer vision approaches specially assisted by deep learning techniques have shown unexpected advancements that practically solve problems that never have been imagined to be automatized like face recognition or automated driving. However, food image recognition due to its high complexity and ambiguity, still remains far from being solved. In this project, we focus on how to combine two challenging research lines: deep learning and uncertainty modeling (epistemic and aleatoric uncertainty) in order to address the food image recognition. We will show the relevance of food ontology and GANs within this research line. After discussing our methodology to advance in this direction, we comment potential applications, as well as social and economic impact of the research on food image analysis.
3D Scene Understanding from a Single Image
Vincent Lepetit
École des Ponts ParisTech
France
Brief Bio
Vincent Lepetit is a director of research at ENPC ParisTech since 2019. Prior to being at ENPC, he was a full professor at the Institute for Computer Graphics and Vision, Graz University of Technology, Austria, and before that, a senior researcher at the Computer Vision Laboratory (CVLab) of EPFL, Switzerland. His research interest are at the interface between Machine Learning and 3D Computer Vision, and currently focus on 3D scene understanding from images. He often serves as an area chair for the major computer vision conferences (CVPR, ICCV, ECCV) and is an associate editor for PAMI, IJCV, and CVIU.
Abstract
3D scene understanding is a long standing, fundamental problem in Computer Vision, where we want to infer a structured semantic representation of the 3D world from 2D images. While humans excel at this task, this is a very challenging problem for computers, as open worlds contain unknown objects seen under unconstrained lighting, and input images only provide projections of the world. Our approach to this problem combines Deep Learning and 3D geometry: In this talk, I will present our recent work on 3D object recognition and pose estimation, on 3D layout (walls, floor, ceiling, ..) reconstruction, and also on self-learning for 3D scene understanding, to compensate for the lack of annotated training data in 3D problems.