4 papers
Weakly Supervised Segmentation as Semantic-Based Regularization
Stefano Colamonaco, Andrei-Bogdan Florea, Jaron Maene
Weakly supervised semantic segmentation (WSSS) trains dense pixel-level segmentation models from partial or coarse annotations such as bounding boxes, scribbles, or image-level tag…
Prototype-Grounded Concept Models for Verifiable Concept Alignment
Stefano Colamonaco, David Debot, Pietro Barbiero +1
Concept Bottleneck Models (CBMs) aim to improve interpretability in Deep Learning by structuring predictions through human-understandable concepts, but they provide no way to verif…
Neurosymbolic Object-Centric Learning with Distant Supervision
Stefano Colamonaco, David Debot, Giuseppe Marra
Neurosymbolic learning can use symbolic rules to provide supervision for latent concepts from weak labels, but it commonly assumes that the entities referenced by these rules are a…
DeepLog: A Software Framework for Modular Neurosymbolic AI
Robin Manhaeve, Stefano Colamonaco, Vincent Derkinderen +4
DeepLog is an operational neurosymbolic framework that unifies logic and deep learning within standard PyTorch workflows. While existing neurosymbolic systems focus on a particular…