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About

Isabel Rio-Torto received the master's degree in Electrical and Computers Engineering in 2019 from the Faculty of Engineering of the University of Porto (FEUP). Isabel is currently a research assistant at INESC TEC, associated with the Visual Computing and Machine Intelligence Group (VCMI), and a Ph.D. student in Computer Science from the Faculty of Sciences of the University of Porto (FCUP). Isabel is also an Invited Teaching Assistant at FEUP, teaching programming courses. Her work is currently focused on "Self-explanatory computer-aided diagnosis with limited supervision".

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Details

Details

Publications

2021

Automatic quality inspection in the automotive industry: a hierarchical approach using simulated data

Authors
Rio-Torto, I; Campanico, AT; Pereira, A; Teixeira, LF; Filipe, V;

Publication
2021 IEEE 8th International Conference on Industrial Engineering and Applications (ICIEA)

Abstract

2021

Improving Automatic Quality Inspection in the Automotive Industry by Combining Simulated and Real Data

Authors
Pinho, P; Rio Torto, I; Teixeira, LF;

Publication
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Abstract
Considerable amounts of data are required for a deep learning model to generalize to unseen cases successfully. Furthermore, such data is often manually labeled, making its annotation process costly and time-consuming. We propose using unlabeled real-world data in conjunction with automatically labeled synthetic data, obtained from simulators, to surpass the increasing need for annotated data. By obtaining real counterparts of simulated samples using CycleGAN and subsequently performing fine-tuning with such samples, we manage to improve a vehicle part’s detection system performance by 2.5%, compared to the baseline exclusively trained on simulated images. We explore adding a semantic consistency loss to CycleGAN by re-utilizing previous work’s trained networks to regularize the conversion process. Moreover, the addition of a post-processing step, which we denominate global NMS, highlights our approach’s effectiveness by better utilizing our detection model’s predictions and ultimately improving the system’s performance by 14.7%. © 2021, Springer Nature Switzerland AG.

2020

Understanding the decisions of CNNs: an in-model approach

Authors
Rio Torto, I; Fernandes, K; Teixeira, LF;

Publication
Pattern Recognition Letters

Abstract

Supervised
thesis

2021

Combining simulated and real images in deep learning

Author
Pedro Xavier Tavares Monteiro Correia de Pinho

Institution
UP-FEUP