13 citations · 14 across the 3 of their papers we have counts for
3 papers
FS-SAM2: Adapting Segment Anything Model 2 for Few-Shot Semantic Segmentation via Low-Rank Adaptation
Bernardo Forni, Gabriele Lombardi, Federico Pozzi +1
Few-shot semantic segmentation has recently attracted great attention. The goal is to develop a model capable of segmenting unseen classes using only a few annotated samples. Most…
TINYCD: A (Not So) Deep Learning Model For Change Detection
Andrea Codegoni, Gabriele Lombardi, Alessandro Ferrari
In this paper, we present a lightweight and effective change detection model, called TinyCD. This model has been designed to be faster and smaller than current state-of-the-art cha…
DANCo: Dimensionality from Angle and Norm Concentration
Claudio Ceruti, Simone Bassis, Alessandro Rozza +3
In the last decades the estimation of the intrinsic dimensionality of a dataset has gained considerable importance. Despite the great deal of research work devoted to this task, mo…