2 citations · 2 across the 2 of their papers we have counts for
3 papers
Toward Reliable Infant Pose Estimation: A Training-Dynamics Approach to Noisy Annotation Detection
Emanuele Cardinale, Sara Moccia, Alessandro Cacciatore +1
Spontaneous movement analysis in preterm infants relies increasingly on markerless pose estimation (PE) to derive clinically relevant motion biomarkers directly from video recordin…
The Blind Spot in 2D Infants' Pose Estimation:Robust Learning from Noisy Annotations
Emanuele Cardinale, Marco Proietti, Alessandro Cacciatore +3
Noisy annotations pose a significant challenge for supervised deep learning, as neural networks rely on large-scale, high-quality labeled data whose corruption can severely impair…
A preliminary study on continual learning in computer vision using Kolmogorov-Arnold Networks
Alessandro Cacciatore, Valerio Morelli, Federica Paganica +3
Deep learning has long been dominated by multi-layer perceptrons (MLPs), which have demonstrated superiority over other optimizable models in various domains. Recently, a new alter…