1 citations · 3 across the 7 of their papers we have counts for
9 papers
Le MuMo JEPA: Multi-Modal Self-Supervised Representation Learning with Learnable Fusion Tokens
Ciem Cornelissen, Sam Leroux, Pieter Simoens
Self-supervised learning has emerged as a powerful paradigm for learning visual representations without manual annotations, yet most methods still operate on a single modality and…
Efficient On-Board Processing of Oblique UAV Video for Rapid Flood Extent Mapping
Vishisht Sharma, Sam Leroux, Lisa Landuyt +2
Effective disaster response relies on rapid disaster response, where oblique aerial video is the primary modality for initial scouting due to its ability to maximize spatial covera…
Privacy-Preserving Computer Vision for Industry: Three Case Studies in Human-Centric Manufacturing
Sander De Coninck, Emilio Gamba, Bart Van Doninck +3
The adoption of AI-powered computer vision in industry is often constrained by the need to balance operational utility with worker privacy. Building on our previously proposed priv…
In-Field Mapping of Grape Yield and Quality with Illumination-Invariant Deep Learning
Ciem Cornelissen, Sander De Coninck, Axel Willekens +2
This paper presents an end-to-end, IoT-enabled robotic system for the non-destructive, real-time, and spatially-resolved mapping of grape yield and quality (Brix, Acidity) in viney…
Enabling Privacy-Aware AI-Based Ergonomic Analysis
Sander De Coninck, Emilio Gamba, Bart Van Doninck +3
Musculoskeletal disorders (MSDs) are a leading cause of injury and productivity loss in the manufacturing industry, incurring substantial economic costs. Ergonomic assessments can…
Adaptive Clustering for Efficient Phenotype Segmentation of UAV Hyperspectral Data
Ciem Cornelissen, Sam Leroux, Pieter Simoens
Unmanned Aerial Vehicles (UAVs) combined with Hyperspectral imaging (HSI) offer potential for environmental and agricultural applications by capturing detailed spectral information…
Reward Machine Inference for Robotic Manipulation
Mattijs Baert, Sam Leroux, Pieter Simoens
Learning from Demonstrations (LfD) and Reinforcement Learning (RL) have enabled robot agents to accomplish complex tasks. Reward Machines (RMs) enhance RL's capability to train pol…
Learning Task Specifications from Demonstrations as Probabilistic Automata
Mattijs Baert, Sam Leroux, Pieter Simoens
Specifying tasks for robotic systems traditionally requires coding expertise, deep domain knowledge, and significant time investment. While learning from demonstration offers a pro…
Mitigating Bias Using Model-Agnostic Data Attribution
Sander De Coninck, Sam Leroux, Pieter Simoens
Mitigating bias in machine learning models is a critical endeavor for ensuring fairness and equity. In this paper, we propose a novel approach to address bias by leveraging pixel i…