7 papers
Understanding How MLLMs Describe Artworks Using Token Activation Maps
Nicola Fanelli, Pasquale De Marinis, Raffaele Scaringi +3
Multimodal Large Language Models (MLLMs) describe artworks with remarkable fluency, yet the visual reasoning behind their outputs remains opaque. When an MLLM names a style, identi…
Take a Peek: Efficient Encoder Adaptation for Few-Shot Semantic Segmentation via LoRA
Pasquale De Marinis, Gennaro Vessio, Giovanna Castellano
Few-shot semantic segmentation (FSS) aims to segment novel classes in query images using only a small annotated support set. While prior research has mainly focused on improving de…
Explainable Fuzzy GNNs for Leak Detection in Water Distribution Networks
Qusai Khaled, Pasquale De Marinis, Moez Louati +3
Timely leak detection in water distribution networks is critical for conserving resources and maintaining operational efficiency. Although Graph Neural Networks (GNNs) excel at cap…
DistillFSS: Synthesizing Few-Shot Knowledge into a Lightweight Segmentation Model
Pasquale De Marinis, Pieter M. Blok, Uzay Kaymak +3
Cross-Domain Few-Shot Semantic Segmentation (CD-FSS) seeks to segment unknown classes in unseen domains using only a few annotated examples. This setting is inherently challenging:…
Matching-Based Few-Shot Semantic Segmentation Models Are Interpretable by Design
Pasquale De Marinis, Uzay Kaymak, Rogier Brussee +2
Few-Shot Semantic Segmentation (FSS) models achieve strong performance in segmenting novel classes with minimal labeled examples, yet their decision-making processes remain largely…
Label Anything: Multi-Class Few-Shot Semantic Segmentation with Visual Prompts
Pasquale De Marinis, Nicola Fanelli, Raffaele Scaringi +4
Few-shot semantic segmentation aims to segment objects from previously unseen classes using only a limited number of labeled examples. In this paper, we introduce Label Anything, a…