Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

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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 ...

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 ...

The ability to train ever-larger neural networks brings artificial intelligence to the forefront of scientific and technical discoveries. However, their exponentially increasing size creates a proportionally greater demand for energy and computational hard ...

Recent studies have reported social cognitive deficits, particularly in emotional processing, in Parkinson's disease (PD). However, a comprehensive characterization of these deficits and their underlying neural correlates remains elusive. Therefore, this s ...

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 ...

Differences in acquisition protocols or hardware result in measurable changes in image characteristics. These differences affect distributional properties and can also affect the spatial and temporal characteristics of the images. Likewise, the distributio ...

Facial paralysis is a highly burdening condition, resulting in a patient's inability to move his musculature on one or both sides of the mouth. The restoration of facial movement is crucial for enhancing the overall patients' quality of life. The use of DE ...

Designing novel materials is greatly dependent on understanding the design principles, physical mechanisms, and modeling methods of material microstructures, requiring experienced designers with expertise and several rounds of trial and error. Although rec ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

This thesis explores the how computational methods can be used to enrich the history of architecture. Using Venice as a case study, it establishes a rich and multifaceted information system, made up of temporal, stylistic, photographic, architectural, and ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

“You never forget how to ride a bike”, – but how is that possible? The brain is able to learn complex skills, stop the practice for years, learn other skills in between, and still retrieve the original knowledge when necessary. The mechanisms of this capab ...

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Predicting human trajectories is essential for the safe operation of autonomous vehicles, yet current data-driven models often lack robustness in case of noisy inputs such as adversarial examples or imperfect observations. Although some trajectory predicti ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Contemporary AI faces important limitations that remain unresolved despite significant successes. Chief among these are the inability to acquire new knowledge without destroying old, the incomprehensible and nonengineerable nature of internal representatio ...

Thermal scene reconstruction holds great potential for various applications, such as building energy analysis and non-destructive infrastructure testing. However, existing methods rely on dense scene measurements and use RGB images for 3D reconstruction, i ...

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 ...

FDTD/FEM simulations, strengthened by machine learning, achieve 95.44% optical absorption efficiency in TiO 2 /TiN plasmonic nanocavities for solar energy harvesting. ...

In this thesis, we study two closely related directions: robustness and generalization in modern deep learning. Deep learning models based on empirical risk minimization are known to be often non-robust to small, worst-case perturbations known as adversari ...

Learning How to Smile: Expression Video Generation With Conditional Adversarial Recurrent Nets

Chattez avec Graph Search

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

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

Training hybrid neural networks with multimode optical nonlinearities using digital twins

Mental State Recognition Deficits Linked to Brain Changes in Parkinson's Disease Without Dementia

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Domain shift, domain adaptation, and generalization: A focus on MRI

DEMO: Dielectric elastomer actuators (DEAs) for the restoration of facial movements

Text-to-Microstructure Generation Using Generative Deep Learning

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

The Skin of Venice: A Computational Approach to the City's Urban Structure

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Context selectivity with dynamic availability enables lifelong continual learning

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Certified Human Trajectory Prediction

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Foundations of a New Learning Paradigm in AI Grounded in the Principles of Evolutionary Developmental Biology

ThermoNeRF: A multimodal Neural Radiance Field for joint RGB-thermal novel view synthesis of building facades

Aggregating Spatial and Photometric Context for Photometric Stereo

Machine-learning-empowered FDTD/FEM simulations for predictive solar energy absorption in plasmonic metamaterial nanocavity arrays

Understanding generalization and robustness in modern deep learning