1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.CV2025
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…
cs.CV2022★ 1 cited
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…