2 papers
cs.CV2026
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…
cs.CV2025
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…