Publications associées à Self-Supervised Underwater Caustics Removal and Descattering via Deep Monocular SLAM

Publications associées (27)

Automated processing of eXplainable Artificial Intelligence outputs in deep learning models for fault diagnostics of large infrastructures

Deep Learning (DL) models processing images to recognize the health state of large infrastructure components can exhibit biases and rely on non-causal shortcuts. eXplainable Artificial Intelligence (XAI) can address these issues but manually analyzing expl ...

2025

Emotion information recovery potential of wav2vec2 network fine-tuned for speech recognition task

Tilak Purohit

Fine-tuning has become a norm to achieve state-of-the-art performance when employing pre-trained networks like foundation models. These models are typically pre-trained on large-scale unannotated data using self-supervised learning (SSL) methods. The SSL-b ...

Institute of Electrical and Electronics Engineers Inc.2025

Nonlinear Effects in Fluctuation Short Pulse Reflectometry

Stefano Coda, Stephan Brunner, Oleg Krutkin

The fluctuation short pulse reflectometry diagnostic is simulated with the full-wave CUWA code for ad-hoc turbulence and simplified geometry. The nonlinear effects on the measurements are investigated while accounting for the plasma curvature, probing angl ...

IOP Publishing Ltd2025

Machine Learning-assisted Sustainable Mix Design of Waste Glass Powder Concrete with Strength-cost-co2 Emissions Trade-offs

Lei Xu

Glass powder, a non-degradable waste material, offers significant potential to reduce cement consumption and carbon emissions in concrete production. However, existing mix design methods for glass powder concrete (GPC) fail to systematically balance econom ...

MDPI2025

Machine learning-aided hysteretic response prediction of double skin composite wall under earthquake loads

Yongtao Wu

Accurately and rapidly simulating the hysteretic behavior of double skin composite wall (DSCW) under earthquake loads enhance the efficiency of seismic performance assessments for high-rise steel-concrete composite structures. The application of artificial ...

2025

Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics

Francesco Croce

Measuring perceptual similarity is a key tool in computer vision. In recent years perceptual metrics based on features extracted from neural networks with large and diverse training sets, e.g. CLIP, have become popular. At the same time, the metrics extrac ...

IEEE2025

SINDER: Repairing the Singular Defects of DINOv2

Mathieu Salzmann, Haoqi Wang

Vision Transformer models trained on large-scale datasets, although effective, often exhibit artifacts in the patch token they extract. While such defects can be alleviated by re-training the entire model with additional classification tokens, the underlyi ...

Springer Nature2025

Meteorological information prediction using images from webcams

Demetri Psaltis, Christophe Moser, Luisa Lambertini, Roy Sarkis

One aspect of the present invention proposes a method of predicting meteorological information by using an artificial deep neural network. The present invention according to one example focuses on image processing, specifically on fusing a plurality of ima ...

2025

Fusing Machine Learning Methods with Computational Fluid Dynamics and Sensor Data for Indoor Air Quality Monitoring

Athanasios Nenes

Monitoring indoor air quality is important nowdays, as noted by many researchers, since pollution potentially introduces a major impact on the health of people. There are many factors that increase the concentration of a pollutant in a room and there may e ...

Institute of Electrical and Electronics Engineers Inc.2025

VIVALDI: steel billet tracking with advanced computer vision

Liza Carol Andrea Dunbar

Our advanced computer vision system allows for the precise tracking of serial numbers on steel billets in challenging industrial settings. It combines cutting-edge hardware and machine learning, excelling in character recognition (99.8%) and localization w ...

SPIE2024

Machine Understanding of Architectural Space: From Analytical to Generative Applications

Mikhael Johanes

The unprecedented development of machine learning (ML) and artificial intelligence (AI) has opened new ways of capturing architectural quality, where large neural networks have demonstrated remarkable capabilities compared to traditional rule-based approac ...

EPFL2024

Second Edition Frcsyn Challenge at Cvpr 2024: Face Recognition Challenge in the Era of Synthetic Data

Alexander Carl Unnervik

Synthetic data is gaining increasing relevance for training machine learning models. This is mainly motivated due to several factors such as the lack of real data and intra-class variability, time and errors produced in manual labeling, and in some cases p ...

IEEE2024

Non-negative matrix factorization-aided phase unmixing and trace element quantification of STEM-EDXS data

Cécile Hébert, Duncan Thomas Lindsay Alexander, James Badro, Farhang Nabiei, Hui Chen

Energy-dispersive X-ray spectroscopy (EDXS) mapping with a scanning transmission electron microscope (STEM) is commonly used for chemical characterization of materials. However, STEM-EDXS quantification becomes challenging when the phases constituting the ...

Elsevier2024

Cross-Age and Cross-Site Domain Shift Impacts on Deep Learning-Based White Matter Fiber Estimation in Newborn and Baby Brains

Jean-Philippe Thiran, Meritxell Bach Cuadra, Rizhong Lin

Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, ...

IEEE2024

Performance of the CMS high-level trigger during LHC Run 2

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk

The CERN LHC provided proton and heavy ion collisions during its Run 2 operation period from 2015 to 2018. Proton-proton collisions reached a peak instantaneous luminosity of 2.1 x 10(34) cm(-2)s(-1), twice the initial design value, at root s = 13 TeV. The ...

IOP Publishing Ltd2024

A Robotic Surgery Platform for Automated Tissue Micromanipulation in Zebrafish Embryos

Mahmut Selman Sakar, Andrew Charles Oates, Ece Özelçi, Laurel Ann Rohde

Microsurgical manipulations are key experimental techniques in life science research, particularly in embryology. These techniques are most often performed manually by highly skilled scientists, posing limitations on speed, precision, and reproducibility. ...

2024

Evidence for tWZ production in proton-proton collisions at √s=13 TeV in multilepton final states

Jan Steggemann, Konstantin Androsov, Anna Mascellani, Joanna Malgorzata Wanczyk

The first evidence for the standard model production of a top quark in association with a W boson and a Z boson is reported. The measurement is performed in multilepton final states, where the Z boson is reconstructed via its decays to electron or muon pai ...

ELSEVIER2024

New Equivalences between Interpolation and SVMs: Kernels and Structured Features

Andrew Duncan Mc Rae

The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space via the kernel trick. Recent work has demonstrated that in certain sufficien ...

SIAM PUBLICATIONS2024

On the Information in Deep Biometric Templates: from Vulnerability of Unprotected Templates to Leakage in Protected Templates

Hatef Otroshi Shahreza

Biometric recognition systems tend toward ubiquity and are widely being used in different applications for authentication purposes. Compared to conventional authentication tools, such as PIN or password, which are always in danger of being forgotten or sto ...

EPFL2024

Machine learning and its impact on psychiatric nosology: Findings from a qualitative study among German and Swiss experts

Georg Michael Joachim Starke

The increasing integration of Machine Learning (ML) techniques into clinical care, driven in particular by Deep Learning (DL) using Artificial Neural Nets (ANNs), promises to reshape medical practice on various levels and across multiple medical fields. Mu ...

2023