Detecting animals in African Savanna with UAVs and the crowds

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Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

Small unmanned aerial vehicles (UAV) have penetrated multiple domains over the past years. In GNSS-denied or indoor environments, aerial robots require a robust and stable localization system, often with external feedback, in order to fly safely. Motion ca ...

Situational awareness by Unmanned Aerial Vehicles (UAVs) is important for many applications such as surveillance, search and rescue, and disaster response. In those applications, detecting and locating people and recognizing their actions in near real-time ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

Advances in camera sensor technology and its manufacturing process now allow high quality image acquisition with low-cost devices. Moreover, the latest significant increase in computational capacity of the processing units enables incorporation of more com ...

We describe and test an obstacle-detection system for small, lake-deployed autonomous surface vehicles (ASVs) that relies on a low-cost, consumer-grade camera and runs on a single-board computer. A key feature of lakes that must be accounted for is the fre ...

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

Unmanned Aerial Vehicles are becoming increasingly popular for a broad variety of tasks ranging from aerial imagery to objects delivery. With the expansion of the areas, where drones can be efficiently used, the collision risk with other flying objects inc ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

Semi-supervised semantic segmentation methods leverage unlabeled data by pseudo-labeling them. Thus the success of these methods hinges on the reliability of the pseudo-labels. Existing methods mostly choose high-confidence pixels in an effort to avoid err ...

We propose an approach for detecting flying objects such as Unmanned Aerial Vehicles (UAVs) and aircrafts when they occupy a small portion of the field of view, possibly moving against complex backgrounds, and are filmed by a camera that itself moves. We a ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

The field of Unmanned Aerial Vehicles (UAVs), also known as drones, is rapidly growing, both in terms of size and of number of applications. Civil applications range from mapping, inspection, search and rescue, taking aerial footage, to art show, entertain ...

Multi-Object Tracking (MOT) is a fundamental computer vision task that involves detecting objects of interest in video frames and associating detections of the same object across time to form trajectories. For association, appearance cues, extracted throug ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

Aerial imagery captured via unmanned aerial vehicles (UAVs) is playing an increasingly important role in disaster response. Unlike satellite imagery, aerial imagery can be captured and processed within hours rather than days. In addition, the spatial resol ...

Unmanned Aerial Vehicles (UAVs) are becoming a significant field of research with numerous applications, ranging from mapping to surveillance. New applications, such as aerial delivery of goods, are expected to appear in the next years and will require mor ...

Data augmentation is vital in deep learning for enhancing model robustness by artificially expanding training datasets. However, advanced methods like CutMix blend images and assign labels based on pixel ratios, often introducing label noise by neglecting ...

This thesis focuses on understanding and improving the reasoning capabilities of neural networks. It develops theoretical results and empirical analyses to uncover reasoning potential and limitations, leveraging these insights to guide the design of improv ...

Detecting animals in African Savanna with UAVs and the crowds

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Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

UWB-based system for UAV Localization in GNSS-Denied Environments: Characterization and Dataset

UAV-Based Situational Awareness System Using Deep Learning

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Smart Camera Architectures for Wireless and Multi-Sensor Vision Applications

Obstacle detection for lake-deployed autonomous surface vehicles using RGB imagery

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

Vision-based detection of aircrafts and UAVs

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

Weighting Pseudo-labels via High-Activation Feature Index Similarity and Object Detection for Semi-supervised Segmentation

Detecting Flying Objects using a Single Moving Camera

Computational design of complex protein folds and soluble analogs of membrane proteins

Vision-Based Sense and Avoid Algorithms for Unmanned Aerial Vehicles

Person Re-Identification and its Application to Multi-Object Tracking

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

Combining Human Computing and Machine Learning to Make Sense of Big (Aerial) Data for Disaster Response

3D Collision Avoidance Algorithm for Unmanned Aerial Vehicles with Limited Field of View Constraints

SaliencyMix plus : Noise-Minimized Image Mixing Method With Saliency Map in Data Augmentation

Learning to Reason with Neural Networks: Principles and Methods