Joint head tracking and pose estimation for visual focus of attention recognition

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Multi-frequency and Doppler spectral radars are valuable tools for retrieving information on the microphysics of precipitation, particularly snowfall. However, estimating microphysical properties from radar measurements is an ill-posed problem, i.e., diffe ...

Derived from the Karush–Kuhn–Tucker (KKT) optimality conditions of utility maximization, the multiple discrete–continuous extreme value (MDCEV) models are extensively used in the field of time use. This is because they are grounded in a framework that maxi ...

Computer vision has made remarkable strides in recent decades, becoming a cornerstone of modern technology with applications ranging from autonomous driving to medical imaging. However, despite these advancements, several core challenges remain, especially ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

We propose a test -time adaptation for 6D object pose tracking that learns to adapt a pre -trained model to track the 6D pose of novel objects. We consider the problem of 6D object pose tracking as a 3D keypoint detection and matching task and present a mo ...

This paper explores data assimilation methods in the context of Bayesian Calibration for fuel performance models. It compares deterministic and stochastic approaches, concentrating on the Generalized Linear Least Squares (GLLS) method and stochastic techni ...

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Current transformer-based skeletal action recognition models tend to focus on a limited set of joints and low-level motion patterns to predict action classes. This results in significant performance degradation under small skeleton perturbations or changin ...

Turning pass-through network architectures into iterative ones, which use their own output as input, is a well-known approach for boosting performance. In this paper, we argue that such architectures offer an additional benefit: The convergence rate of the ...

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Effective Prognostics and Health Management (PHM) relies on accurate prediction of the Remaining Useful Life (RUL). Data-driven RUL prediction techniques rely heavily on the representativeness of the available time-to-failure trajectories. Therefore, these ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Viewers of 360-degree videos are provided with both visual modality to characterize their surrounding views and audio modality to indicate the sound direction. Though both modalities are important for saliency prediction, little work has been done by joint ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Understanding visual complexity of urban environments may improve urban design strategies and limit visual pollution due to advertising, road signage, telecommunication systems and machinery. This paper aims at quantifying visual complexity specifically in ...

Video DeepFakes are fake media created with Deep Learning (DL) that manipulate a person’s expression or identity. Most current DeepFake detection methods analyze each frame independently, ignoring inconsistencies and unnatural movements between frames. Som ...

Human motion analysis and synthesis is integral to many computer vision applications, from autonomous driving to sports analysis. In this thesis, we address several problems in this domain. First we consider active viewpoint selection for pose estimation w ...

Joint head tracking and pose estimation for visual focus of attention recognition

Chattez avec Graph Search

What can radars tell us about snowfall microphysics? Insights from a Markov chain Monte-Carlo approach

A new flexible multiple discrete–continuous extreme value (MDCEV) choice model: exploring non-monotonic preference in time use decisions

Improving Human Pose & Shape Estimation with Explicit and Implicit Priors

Aggregating Spatial and Photometric Context for Photometric Stereo

Test-time adaptation for 6D pose tracking

Comparison of Bayesian Model Calibration Techniques for Future Application to Fuel Performance Behavior Models

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Robust machine learning for neuroscientific inference

MaskCLR: Attention-Guided Contrastive Learning for Robust Action Representation Learning

Enabling Uncertainty Estimation in Iterative Neural Networks

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Domain adaptation via alignment of operation profile for Remaining Useful Lifetime prediction

Incorporating Projective Geometry into Deep Learning

SVGC-AVA: 360-Degree Video Saliency Prediction With Spherical Vector-Based Graph Convolution and Audio-Visual Attention

Digital Twins of Stone Masonry Buildings for Damage Assessment

On distributional autoregression and iterated transportation

Visual complexity of urban streetscapes: human vs computer vision

Efficient Temporally-Aware DeepFake Detection using H.264 Motion Vectors

Automated Human Motion Analysis and Synthesis