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Large-scale detection of marine debris in coastal areas with Sentinel-2

Publications associées (29)

Rigidity-Aware Detection for 6D Object Pose Estimation

Mathieu Salzmann, Yinlin Hu, Rui Song, Jingyu Li

Most recent 6D object pose estimation methods first use object detection to obtain 2D bounding boxes before actually regressing the pose. However, the general object detection methods they use are ill-suited to handle cluttered scenes, thus producing poor ...

Ieee Computer Soc2023

Breaking the Curse of Dimensionality in Deep Neural Networks by Learning Invariant Representations

Leonardo Petrini

Artificial intelligence, particularly the subfield of machine learning, has seen a paradigm shift towards data-driven models that learn from and adapt to data. This has resulted in unprecedented advancements in various domains such as natural language proc ...

EPFL2023

Addressing chemical pollution in biodiversity research

Kristin Schirmer, Colette vom Berg, Ahmed Tlili

Climate change, biodiversity loss, and chemical pollution are planetary-scale emergencies requiring urgent mitigation actions. As these "triple crises" are deeply interlinked, they need to be tackled in an integrative manner. However, while climate change ...

WILEY2023

Why is the winner the best?

Adrien Raphaël Depeursinge, Jian Wang, Ho Ling Li, Gabriel Girard, Yong Yang, Fan Xia, Xiao Wang, Hui Wang, Jing Li

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really gener ...

Ieee Computer Soc2023

Self-supervised Dense Representation Learning for Live-Cell Microscopy with Time Arrow Prediction

Max Stieber, Martin Weigert, Benjamin Tobias Gallusser

State-of-the-art object detection and segmentation methods for microscopy images rely on supervised machine learning, which requires laborious manual annotation of training data. Here we present a self-supervised method based on time arrow prediction pre-t ...

Springer International Publishing Ag2023

How deep convolutional neural networks lose spatial information with training

Matthieu Wyart, Leonardo Petrini, Umberto Maria Tomasini, Francesco Cagnetta

A central question of machine learning is how deep nets manage to learn tasks in high dimensions. An appealing hypothesis is that they achieve this feat by building a representation of the data where information irrelevant to the task is lost. For image da ...

Iop Publishing Ltd2023

On the role of system integration in plastic waste management

François Maréchal, Ivan Daniel Kantor, Julia Granacher

The management of plastic waste is a growing concern that can harm wildlife and ecosystems, and its end-of-life policies are key to mitigating plastic environmental impact. This work presents a decision-making methodology for the thermal and material valor ...

Elsevier2023

Trusting the Explainers: Teacher Validation of Explainable Artificial Intelligence for Course Design

Tanja Christina Käser Jacober, Vinitra Swamy, Mirko Marras, Sijia Du

Deep learning models for learning analytics have become increasingly popular over the last few years; however, these approaches are still not widely adopted in real-world settings, likely due to a lack of trust and transparency. In this paper, we tackle th ...

2023

Probabilistic methods for neural combinatorial optimization

Nikolaos Karalias

The monumental progress in the development of machine learning models has led to a plethora of applications with transformative effects in engineering and science. This has also turned the attention of the research community towards the pursuit of construc ...

EPFL2023