A Performance Evaluation of Local Features for Image-Based 3D Reconstruction

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This paper investigates the presence of OCR-sensitive neurons within the Transformer architecture and their influence on named entity recognition (NER) performance on historical documents. By analysing neuron activation patterns in response to clean and no ...

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 ...

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

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 ...

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 ...

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 ...

The image compression field is witnessing a shift in paradigm thanks to the rise of neural network-based models. In this context, the JPEG committee is in the process of standardizing the first learning-based image compression standard, known as JPEG AI. W ...

Neurodegenerative and neuroinflammatory disorders often involve complex pathophysiological mechanisms that are â to this date â only partially understood. A more comprehensive understanding of those microstructural processes and their characterization ...

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 ...

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 ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

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 ...

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 ...

Whereas pulse-echo ultrasound imaging relied on focused acoustic waves since its inception, the last two decades have seen the development of techniques based on unfocused waves, including ultrafast ultrasound imaging. In large part due to the emergence of ...

The space industry has experienced substantial growth in recent years, leading to rapid advancements in space exploration and space-based technologies. Consequently, the study of electronics and sensor performance in extreme environments has become crucial ...

Recent developments in neural architecture search (NAS) emphasize the significance of considering robust architectures against malicious data. However, there is a notable absence of benchmark evaluations and theoretical guarantees for searching these robus ...

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimen ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

Distributed learning is the key for enabling training of modern large-scale machine learning models, through parallelising the learning process. Collaborative learning is essential for learning from privacy-sensitive data that is distributed across various ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

A Performance Evaluation of Local Features for Image-Based 3D Reconstruction

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Investigating OCR-Sensitive Neurons to Improve Entity Recognition in Historical Documents

Aggregating Spatial and Photometric Context for Photometric Stereo

Towards Trustworthy Deep Learning for Image Reconstruction

Incorporating Projective Geometry into Deep Learning

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

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

On the impact of learning-based image compression on computer vision tasks

Development and clinical validation of computational imaging biomarkers for neurodegenerative diseases

Understanding generalization and robustness in modern deep learning

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

Robust machine learning for neuroscientific inference

Enabling Uncertainty Estimation in Iterative Neural Networks

Inverse Problems in Pulse-Echo Ultrasound: Exploiting Coherence for Image Enhancement and Quantitative Imaging

Single-Photon Avalanche Diode Image Sensors for Harsh Radiation Environments

Robust NAS under adversarial training: benchmark, theory, and beyond

The multimodality cell segmentation challenge: toward universal solutions

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Optimization Algorithms for Decentralized, Distributed and Collaborative Machine Learning

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange