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Publications

Publications by CTM

2024

Intrinsic Explainability for End-to-End Object Detection

Authors
Fernandes, L; Fernandes, JND; Calado, M; Pinto, JR; Cerqueira, R; Cardoso, JS;

Publication
IEEE ACCESS

Abstract
Deep Learning models are automating many daily routine tasks, indicating that in the future, even high-risk tasks will be automated, such as healthcare and automated driving areas. However, due to the complexity of such deep learning models, it is challenging to understand their reasoning. Furthermore, the black box nature of the designed deep learning models may undermine public confidence in critical areas. Current efforts on intrinsically interpretable models focus only on classification tasks, leaving a gap in models for object detection. Therefore, this paper proposes a deep learning model that is intrinsically explainable for the object detection task. The chosen design for such a model is a combination of the well-known Faster-RCNN model with the ProtoPNet model. For the Explainable AI experiments, the chosen performance metric was the similarity score from the ProtoPNet model. Our experiments show that this combination leads to a deep learning model that is able to explain its classifications, with similarity scores, using a visual bag of words, which are called prototypes, that are learned during the training process. Furthermore, the adoption of such an explainable method does not seem to hinder the performance of the proposed model, which achieved a mAP of 69% in the KITTI dataset and a mAP of 66% in the GRAZPEDWRI-DX dataset. Moreover, our explanations have shown a high reliability on the similarity score.

2024

YOLOMM - You Only Look Once for Multi-modal Multi-tasking

Authors
Campos, F; Cerqueira, FG; Cruz, RPM; Cardoso, JS;

Publication
PROGRESS IN PATTERN RECOGNITION, IMAGE ANALYSIS, COMPUTER VISION, AND APPLICATIONS, CIARP 2023, PT I

Abstract
Autonomous driving can reduce the number of road accidents due to human error and result in safer roads. One important part of the system is the perception unit, which provides information about the environment surrounding the car. Currently, most manufacturers are using not only RGB cameras, which are passive sensors that capture light already in the environment but also Lidar. This sensor actively emits laser pulses to a surface or object and measures reflection and time-of-flight. Previous work, YOLOP, already proposed a model for object detection and semantic segmentation, but only using RGB. This work extends it for Lidar and evaluates performance on KITTI, a public autonomous driving dataset. The implementation shows improved precision across all objects of different sizes. The implementation is entirely made available: https://github.com/filipepcampos/yolomm.

2024

Second Edition FRCSyn Challenge at CVPR 2024: Face Recognition Challenge in the Era of Synthetic Data

Authors
Tame, ID; Tolosana, R; Melzi, P; Rodríguez, RV; Kim, M; Rathgeb, C; Liu, X; Morales, A; Fiérrez, J; Garcia, JO; Zhong, Z; Huang, Y; Mi, Y; Ding, S; Zhou, S; He, S; Fu, L; Cong, H; Zhang, R; Xiao, Z; Smirnov, E; Pimenov, A; Grigorev, A; Timoshenko, D; Asfaw, KM; Low, CY; Liu, H; Wang, C; Zuo, Q; He, Z; Shahreza, HO; George, A; Unnervik, A; Rahimi, P; Marcel, S; Neto, PC; Huber, M; Kolf, JN; Damer, N; Boutros, F; Cardoso, JS; Sequeira, AF; Atzori, A; Fenu, G; Marras, M; Struc, V; Yu, J; Li, Z; Li, J; Zhao, W; Lei, Z; Zhu, X; Zhang, XY; Biesseck, B; Vidal, P; Coelho, L; Granada, R; Menotti, D;

Publication
CoRR

Abstract

2024

Explainable Deep Learning Methods in Medical Image Classification: A Survey

Authors
Patrício, C; Neves, C; Teixeira, F;

Publication
ACM COMPUTING SURVEYS

Abstract
The remarkable success of deep learning has prompted interest in its application to medical imaging diagnosis. Even though state-of-the-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box nature of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical imaging diagnosis, including visual, textual, example-based and concept-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations. In addition, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging and the future research directions on the topic are discussed.

2024

Systematic review on weapon detection in surveillance footage through deep learning

Authors
Santos, T; Oliveira, H; Cunha, A;

Publication
COMPUTER SCIENCE REVIEW

Abstract
In recent years, the number of crimes with weapons has grown on a large scale worldwide, mainly in locations where enforcement is lacking or possessing weapons is legal. It is necessary to combat this type of criminal activity to identify criminal behavior early and allow police and law enforcement agencies immediate action.Despite the human visual structure being highly evolved and able to process images quickly and accurately if an individual watches something very similar for a long time, there is a possibility of slowness and lack of attention. In addition, large surveillance systems with numerous equipment require a surveillance team, which increases the cost of operation. There are several solutions for automatic weapon detection based on computer vision; however, these have limited performance in challenging contexts.A systematic review of the current literature on deep learning-based weapon detection was conducted to identify the methods used, the main characteristics of the existing datasets, and the main problems in the area of automatic weapon detection. The most used models were the Faster R-CNN and the YOLO architecture. The use of realistic images and synthetic data showed improved performance. Several challenges were identified in weapon detection, such as poor lighting conditions and the difficulty of small weapon detection, the last being the most prominent. Finally, some future directions are outlined with a special focus on small weapon detection.

2024

Towards truly sustainable IoT systems: the SUPERIOT project

Authors
Katz, M; Paso, T; Mikhaylov, K; Pessoa, L; Fontes, H; Hakola, L; Leppaeniemi, J; Carlos, E; Dolmans, G; Rufo, J; Drzewiecki, M; Sallouha, H; Napier, B; Branquinho, A; Eder, K;

Publication
JOURNAL OF PHYSICS-PHOTONICS

Abstract
This paper provides an overview of the SUPERIOT project, an EU SNS JU (Smart Networks and Services Joint Undertaking) initiative focused on developing truly sustainable IoT systems. The SUPERIOT concept is based on a unique holistic approach to sustainability, proactively developing sustainable solutions considering the design, implementation, usage and disposal/reuse stages. The concept exploits radio and optical technologies to provide dual-mode wireless connectivity and dual-mode energy harvesting as well as dual-mode IoT node positioning. The implementation of the IoT nodes or devices will maximize the use of sustainable printed electronics technologies, including printed components, conductive inks and substrates. The paper describes the SUPERIOT concept, covering the key technical approaches to be used, promising scenarios and applications, project goals and demonstrators which will be developed to the proof-of-concept stage. In addition, the paper briefly discusses some important visions on how this technology may be further developed in the future.

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