2 papers
cs.LG2025
eXIAA: eXplainable Injections for Adversarial Attack
Leonardo Pesce, Jiawen Wei, Gianmarco Mengaldo
Post-hoc explainability methods are a subset of Machine Learning (ML) that aim to provide a reason for why a model behaves in a certain way. In this paper, we show a new black-box…
cs.CV2025
WS: Weakly Supervised Segmentation using Before-After Supervision in Waste Sorting
Andrea Marelli, Alberto Foresti, Leonardo Pesce +2
In industrial quality control, to visually recognize unwanted items within a moving heterogeneous stream, human operators are often still indispensable. Waste-sorting stands as a s…