27 citations · 31 across the 11 of their papers we have counts for
7 papers · 1 filter
Enhancing Tree Species Classification: Insights from YOLOv8 and Explainable AI Applied to TLS Point Cloud Projections
Adrian Straker, Paul Magdon, Marco Zullich +5
Aiming to advance research in the field of interpretability of deep learning models for tree species classification using TLS 3D point clouds we present insights in the classificat…
Uncertainty in Semantic Language Modeling with PIXELS
Stefania Radu, Marco Zullich, Matias Valdenegro-Toro
Pixel-based language models aim to solve the vocabulary bottleneck problem in language modeling, but the challenge of uncertainty quantification remains open. The novelty of this w…
Sparsity-Driven Plasticity in Multi-Task Reinforcement Learning
Aleksandar Todorov, Juan Cardenas-Cartagena, Rafael F. Cunha +2
Plasticity loss, a diminishing capacity to adapt as training progresses, is a critical challenge in deep reinforcement learning. We examine this issue in multi-task reinforcement l…
Explainable Artificial Intelligence Techniques for Interpretation of Food Models: a Review
Leonardo Arrighi, Ingrid Alves de Moraes, Marco Zullich +3
Artificial Intelligence (AI) has become essential for analyzing complex data and solving highly-challenging tasks. It is being applied across numerous disciplines beyond computer s…
Can Bayesian Neural Networks Explicitly Model Input Uncertainty?
Matias Valdenegro-Toro, Marco Zullich
Inputs to machine learning models can have associated noise or uncertainties, but they are often ignored and not modelled. It is unknown if Bayesian Neural Networks and their appro…
Large-image Object Detection for Fine-grained Recognition of Punches Patterns in Medieval Panel Painting
Josh Bruegger, Diana Ioana Catana, Vanja Macovaz +3
The attribution of the author of an art piece is typically a laborious manual process, usually relying on subjective evaluations of expert figures. However, there are some situatio…
Adaptive Prompt Tuning: Vision Guided Prompt Tuning with Cross-Attention for Fine-Grained Few-Shot Learning
Eric Brouwer, Jan Erik van Woerden, Gertjan Burghouts +2
Few-shot, fine-grained classification in computer vision poses significant challenges due to the need to differentiate subtle class distinctions with limited data. This paper prese…