Counting People by Estimating People Flows

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The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, in ...

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 ...

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 ...

The field of biometrics, and especially face recognition, has seen a wide-spread adoption the last few years, from access control on personal devices such as phones and laptops, to automated border controls such as in airports. The stakes are increasingly ...

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 ...

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 ...

The evolution of edge computing has advanced the accessibility of E-health recommendation services, encompassing areas such as medical consultations, prescription guidance, and diagnostic assessments. Traditional methodologies predominantly utilize central ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

We consider the problem of learning a target function corresponding to a deep, extensive-width, non-linear neural network with random Gaussian weights. We consider the asymptotic limit where the number of samples, the input dimension and the network width ...

We present a framework for performing regression when both covariate and response are probability distributions on a compact and convex subset of

\R^d

. Our regression model is based on the theory of optimal transport and links the conditional Fr'echet m ...

In this study, we examine the potential of several self-supervised deep learning models in predicting forest attributes and detecting forest changes using ESA Sentinel-1 and Sentinel-2 images. The performance of the proposed deep learning models is compare ...

The successes of deep learning for semantic segmentation can in be, in part, attributed to its scale: a notion that encapsulates the largeness of these computational architectures and the labeled datasets they are trained on. These resource requirements hi ...

Counting People by Estimating People Flows

Chat with Graph Search

GraphFedAI framework for DDoS attack detection in IoT systems using federated learning and graph based artificial intelligence

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

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

Performing and Detecting Backdoor Attacks on Face Recognition Algorithms

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

Enabling Uncertainty Estimation in Iterative Neural Networks

On-device diagnostic recommendation with heterogeneous federated BlockNets

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Bayes-optimal Learning of Deep Random Networks of Extensive-width

Distributional Regression and Autoregression via Optimal Transport

Semi-Supervised Deep Learning Representations in Earth Observation Based Forest Management

Confidence Matters: Applications to Semantic Segmentation