activity
20242026
collaborators

11 papers

cs.CV2026

One Patch to Caption Them All: A Unified Zero-Shot Captioning Framework

Lorenzo Bianchi, Giacomo Pacini, Fabio Carrara +3

Zero-shot captioners are recently proposed models that utilize common-space vision-language representations to caption images without relying on paired image-text data. To caption…

cs.CV2026

Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training

Luca Ciampi, Gabriele Lagani, Giuseppe Amato +1

Supervised deep learning for semantic segmentation has achieved excellent results in accurately identifying anatomical and pathological structures in medical images. However, it of…

cs.CV2026

A Survey on Class-Agnostic Counting: Advancements from Reference-Based to Open-World Text-Guided Approaches

Luca Ciampi, Ali Azmoudeh, Elif Ecem Akbaba +5

Visual object counting has recently shifted towards class-agnostic counting (CAC), which addresses the challenge of counting objects across arbitrary categories, a crucial capabili…

cs.CV2025

Learning Egocentric In-Hand Object Segmentation through Weak Supervision from Human Narrations

Nicola Messina, Rosario Leonardi, Luca Ciampi +4

Pixel-level recognition of objects manipulated by the user from egocentric images enables key applications spanning assistive technologies, industrial safety, and activity monitori…

cs.CV2025

CountingDINO: A Training-free Pipeline for Class-Agnostic Counting using Unsupervised Backbones

Giacomo Pacini, Lorenzo Bianchi, Luca Ciampi +3

Class-agnostic counting (CAC) aims to estimate the number of objects in images without being restricted to predefined categories. However, while current exemplar-based CAC methods…

cs.CV2025

Talking to DINO: Bridging Self-Supervised Vision Backbones with Language for Open-Vocabulary Segmentation

Luca Barsellotti, Lorenzo Bianchi, Nicola Messina +5

Open-Vocabulary Segmentation (OVS) aims at segmenting images from free-form textual concepts without predefined training classes. While existing vision-language models such as CLIP…