11 papers
Does it Really Count? Assessing Semantic Grounding in Text-Guided Class-Agnostic Counting
Giacomo Pacini, Luca Ciampi, Nicola Messina +3
Open-world text-guided class-agnostic counting (CAC) has emerged as a flexible paradigm for counting arbitrary object classes by using natural language prompts. However, current ev…
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…
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…
Neuro-Inspired Visual Pattern Recognition via Biological Reservoir Computing
Luca Ciampi, Ludovico Iannello, Fabrizio Tonelli +4
In this paper, we present a neuro-inspired approach to reservoir computing (RC) in which a network of in vitro cultured cortical neurons serves as the physical reservoir. Rather th…
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…
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…