Publications related to Wide-Depth-Range 6D Object Pose Estimation in Space

Related publications (26)

From Pose to Behavior: Advancing Multi-Individual Pose Estimation and Hierarchical Action Segmentation with Transformers

Accurately measuring behavior is critical to understanding brain function. While traditional behavioral analysis has relied on human observation and manual processing, advances in machine learning and computer vision have begun to automate the field. This ...

EPFL2025

EntitySAM: Segment Everything in Video

Mingqiao Ye

Automatically tracking and segmenting every video entity remains a significant challenge. Despite rapid advancements in video segmentation, even state-of-the-art models like SAM 2 struggle to consistently track all entities across a video—a task we refer t ...

IEEE2025

Machine learning enhanced tomographic reconstruction for multispectral imaging on TCV

Basil Duval, Artur Perek

A multispectral camera setup is used to infer a 2D map of plasma parameters in a tokamak from spectral emissions. However, the light measured by these cameras is line integrated in the toroidal direction, whereas emissivities on the poloidal plane are nece ...

2025

Fully data-driven and modular building thermal control with physically consistent modeling

Benjamin Bejar Haro, Carl Pierre Jacques Remlinger

Machine learning has experienced significant growth in the smart building sector, whether for building modeling or energy management. Data-driven approaches leverage available measurements to bypass the slow and costly calibration of physics-based models, ...

2025

Explainable AI guided unsupervised fault diagnostics for high-voltage circuit breakers

Olga Fink, Gaëtan Michel Frusque, Florent Evariste Forest

Commercial high-voltage circuit breaker (CB) condition monitoring systems rely on directly observable physical parameters such as gas filling pressure with pre-defined thresholds. While these parameters are crucial, they only cover a small subset of malfun ...

2025

Rapid prediction of phonon density of states by crystal attention graph neural network and high-throughput screening of candidate substrates for wide bandgap electronic cooling

Changpeng Lin

Machine learning has demonstrated superior performance in predicting vast materials properties. However, predicting a spectral-like continuous material property such as phonon density of states (DOS) is more challenging for machine learning. In this work, ...

2025

NNG-Mix: Improving Semi-Supervised Anomaly Detection with Pseudo-Anomaly Generation

Olga Fink, Gaëtan Michel Frusque

Anomaly detection (AD) is essential in identifying rare and often critical events in complex systems, finding applications in fields such as network intrusion detection, financial fraud detection, and fault detection in infrastructure and industrial system ...

2024

Prediction of mycotoxin response of DNA-wrapped nanotube sensor with machine learning

Ardemis Anoush Boghossian, Sara Behjati, Sayyed Hashem Sajjadi, Yahya Rabbani

DNA-wrapped single-walled carbon nanotubes (DNA-SWCNTs) have demonstrated great versatility as optical sensors. SWCNTs emit a near-infrared fluorescence that is responsive to the slightest changes in their environment, enabling the creation of sensors that ...

2023

Hardware-Software Co-design for Improved Resource Utilization in DNN Accelerators

Ahmet Caner Yüzügüler

Deep neural networks (DNN) have become an essential tool to tackle challenging tasks in many fields of computer science. However, their high computational complexity limits their applicability. Specialized DNN accelerators have been developed to accommodat ...

EPFL2023

Data analysis techniques for the visualization and classification of historical vehicle engines' health status using data-driven solutions

Alejandro Roda Buch

In the field of cultural heritage, the use of non-destructive techniques to determine the state ofconservation of an artifact is of the utmost importance, to avoid damage to the object itself. In this paper, we present a data pipeline and several machine l ...

ASSOC PROFISSIONAL CONSERVADORES-RESTAURADORES PORTUGAL2023

Exploring quantum perceptron and quantum neural network structures with a teacher-student scheme

Patrick Hümbeli

Near-term quantum devices can be used to build quantum machine learning models, such as quantum kernel methods and quantum neural networks (QNN), to perform classification tasks. There have been many proposals on how to use variational quantum circuits as ...

2022

Reviewing the application of machine learning methods to model urban form indicators in planning decision support systems : potential, issues and challenges

Jérôme Chenal, Rémi Jaligot

Modern cities dynamically face several challenges including digitalization, sustainability, resilience and economic development. Urban planners and designers must develop urban forms that address these challenges. With the integration of new communication ...

2022

Machine Learning Models for Mycobacterium tuberculosis In Vitro Activity: Prediction and Target Visualization

Anthony Vocat

Tuberculosis (TB) is a major global health challenge, with approximately 1.4 million deaths per year. There is still a need to develop novel treatments for patients infected with Mycobacterium tuberculosis (Mtb). There have been many large-scale phenotypic ...

AMER CHEMICAL SOC2022

Pedestrian Stop and Go Forecasting with Hybrid Feature Fusion

Alexandre Massoud Alahi, Taylor Ferdinand Mordan

Forecasting pedestrians' future motions is essential for autonomous driving systems to safely navigate in urban areas. However, existing prediction algorithms often overly rely on past observed trajectories and tend to fail around abrupt dynamic changes, s ...

2022

Multi-agent maintenance scheduling of generation unit in electricity market using safe deep reinforcement learning algorithm

Olga Fink

Improving maintenance scheduling of generation units in an electricity market would increase the safety and reliability of the system. This problem can be modeled as a multi-agent bi-level decision-making problem associated with some safety constraints. In ...

2022

Data-driven myelin water imaging based on T-1 and T-2 relaxometry

Tobias Kober, Tom Hilbert, Erick Jorge Canales Rodriguez, Marco Pizzolato

Long acquisition times preclude the application of multiecho spin echo (MESE) sequences for myelin water fraction (MWF) mapping in daily clinical practice. In search of alternative methods, previous studies of interest explored the biophysical modeling of ...

WILEY2021

ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals

David Atienza Alonso, Arman Iranfar, Adriana Arza Valdes

2021

Neural interface systems with on-device computing: machine learning and neuromorphic architectures

Mahsa Shoaran

Development of neural interface and brain-machine interface (BMI) systems enables the treatment of neurological disorders including cognitive, sensory, and motor dysfunctions. While neural interfaces have steadily decreased in form factor, recent developme ...

CURRENT BIOLOGY LTD2021

Beyond Cameras: Real-Time High-Resolution 3D and Panoramic MultiView Vision Systems with Their Applications

Bilal Demir

The cameras are invented by imitating the human visual system to capture the scene. The camera technologies have been substantially advanced in recent years. 108 MP resolution with 100x hybrid zoom has become standard features for smartphone flagships. In ...

EPFL2020

Artificial Neural Networks for Impact Position Detection in Haptic Surfaces

Yves Perriard, Paolo Germano, Camilo Hernandez Mejia

Recently, the interest in haptic feedback is growing thanks to its ability to enhance the interaction with Human Machine Interfaces (HMIs). This research project is exploring the potential of machine learning combined with piezoelectric actuators to genera ...

IEEE2019