69 citations · 144 across the 15 of their papers we have counts for
21 papers
Unifying Runtime Monitoring Approaches for Safety-Critical Machine Learning: Application to Vision-Based Landing
Mathieu Dario, Florent Chenevier, Kévin Delmas +2
Runtime monitoring is essential to ensure the safety of ML applications in safety-critical domains. However, current research is fragmented, with independent methods emerging from…
Spatially-constrained clustering of geospatial features for heat vulnerability assessment of favelas in Rio de Janeiro
Baptiste Clemence, Thomas Hallopeau, Vanderlei Pascoal De Matos +2
Informal settlements face disproportionate exposure to climate-related health hazards. However, existing methodologies lack systematic approaches to link diverse settlement charact…
Not every day is a sunny day: Synthetic cloud injection for deep land cover segmentation robustness evaluation across data sources
Sara Mobsite, Renaud Hostache, Laure Berti Equille +2
Supervised deep learning for land cover semantic segmentation (LCS) relies on labeled satellite data. However, most existing Sentinel-2 datasets are cloud-free, which limits their…
Vision Backbone Efficient Selection for Image Classification in Low-Data Regimes
Joris Guerin, Shray Bansal, Amirreza Shaban +2
Transfer learning has become an essential tool in modern computer vision, allowing practitioners to leverage backbones, pretrained on large datasets, to train successful models fro…
Combining Two Adversarial Attacks Against Person Re-Identification Systems
Eduardo de O. Andrade, Igor Garcia Ballhausen Sampaio, Joris Guérin +1
The field of Person Re-Identification (Re-ID) has received much attention recently, driven by the progress of deep neural networks, especially for image classification. The problem…
Benchmarking person re-identification datasets and approaches for practical real-world implementations
Jose Huaman, Felix O. Sumari, Luigy Machaca +2
Recently, Person Re-Identification (Re-ID) has received a lot of attention. Large datasets containing labeled images of various individuals have been released, allowing researchers…
TrADe Re-ID -- Live Person Re-Identification using Tracking and Anomaly Detection
Luigy Machaca, F. Oliver Sumari H, Jose Huaman +2
Person Re-Identification (Re-ID) aims to search for a person of interest (query) in a network of cameras. In the classic Re-ID setting the query is sought in a gallery containing p…
Unifying Evaluation of Machine Learning Safety Monitors
Joris Guerin, Raul Sena Ferreira, Kevin Delmas +1
With the increasing use of Machine Learning (ML) in critical autonomous systems, runtime monitors have been developed to detect prediction errors and keep the system in a safe stat…
Evaluation of Runtime Monitoring for UAV Emergency Landing
Joris Guerin, Kevin Delmas, Jérémie Guiochet
To certify UAV operations in populated areas, risk mitigation strategies -- such as Emergency Landing (EL) -- must be in place to account for potential failures. EL aims at reducin…
Certifying Emergency Landing for Safe Urban UAV
Joris Guerin, Kevin Delmas, Jérémie Guiochet
Unmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments. However, allowing UAVs to fly above densely populated areas raises concer…
A novel method for object detection using deep learning and CAD models
Igor Garcia Ballhausen Sampaio, Luigy Machaca, José Viterbo +1
Object Detection (OD) is an important computer vision problem for industry, which can be used for quality control in the production lines, among other applications. Recently, Deep…
Combining pretrained CNN feature extractors to enhance clustering of complex natural images
Joris Guerin, Stephane Thiery, Eric Nyiri +2
Recently, a common starting point for solving complex unsupervised image classification tasks is to use generic features, extracted with deep Convolutional Neural Networks (CNN) pr…
Robust Detection of Objects under Periodic Motion with Gaussian Process Filtering
Joris Guerin, Anne Magaly de Paula Canuto, Luiz Marcos Garcia Goncalves
Object Detection (OD) is an important task in Computer Vision with many practical applications. For some use cases, OD must be done on videos, where the object of interest has a pe…
Towards Practical Implementations of Person Re-Identification from Full Video Frames
Felix O. Sumari, Luigy Machaca, Jose Huaman +2
With the major adoption of automation for cities security, person re-identification (Re-ID) has been extensively studied recently. In this paper, we argue that the current way of s…
Forecasting Covid-19 dynamics in Brazil: a data driven approach
Igor G. Pereira, Joris M. Guerin, Andouglas G. Silva Junior +3
This paper has a twofold contribution. The first is a data driven approach for predicting the Covid 19 pandemic dynamics, based on data from more advanced countries. The second is…
Semantically Meaningful View Selection
Joris Guérin, Olivier Gibaru, Eric Nyiri +2
An understanding of the nature of objects could help robots to solve both high-level abstract tasks and improve performance at lower-level concrete tasks. Although deep learning ha…
Improving Image Clustering With Multiple Pretrained CNN Feature Extractors
Joris Guérin, Byron Boots
For many image clustering problems, replacing raw image data with features extracted by a pretrained convolutional neural network (CNN), leads to better clustering performance. How…
Unsupervised robotic sorting: Towards autonomous decision making robots
Joris Guérin, Stéphane Thiery, Eric Nyiri +1
Autonomous sorting is a crucial task in industrial robotics which can be very challenging depending on the expected amount of automation. Usually, to decide where to sort an object…
CNN features are also great at unsupervised classification
Joris Guérin, Olivier Gibaru, Stéphane Thiery +1
This paper aims at providing insight on the transferability of deep CNN features to unsupervised problems. We study the impact of different pretrained CNN feature extractors on the…
Clustering for Different Scales of Measurement - the Gap-Ratio Weighted K-means Algorithm
Joris Guérin, Olivier Gibaru, Stéphane Thiery +1
This paper describes a method for clustering data that are spread out over large regions and which dimensions are on different scales of measurement. Such an algorithm was develope…
Learning local trajectories for high precision robotic tasks : application to KUKA LBR iiwa Cartesian positioning
Joris Guerin, Olivier Gibaru, Eric Nyiri +1
To ease the development of robot learning in industry, two conditions need to be fulfilled. Manipulators must be able to learn high accuracy and precision tasks while being safe fo…