33 citations · 51 across the 19 of their papers we have counts for
20 papers
Agentic Multimodal Models for Environmental Hyperspectral Unmixing
Michał Cholewa, Luca Ciampi, Nicola Messina +2
Hyperspectral unmixing is a key task in remote sensing that aims to decompose mixed pixels in hyperspectral images into their constituent material signatures, or endmembers, and th…
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…
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…
From Neural Activity to Computation: Biological Reservoirs for Pattern Recognition in Digit Classification
Ludovico Iannello, Luca Ciampi, Fabrizio Tonelli +5
In this paper, we present a biologically grounded approach to reservoir computing (RC), in which a network of cultured biological neurons serves as the reservoir substrate. This sy…
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…
From Neurons to Computation: Biological Reservoir Computing for Pattern Recognition
Ludovico Iannello, Luca Ciampi, Gabriele Lagani +6
In this paper, we introduce a paradigm for reservoir computing (RC) that leverages a pool of cultured biological neurons as the reservoir substrate, creating a biological reservoir…
Comparison of Different Deep Neural Network Models in the Cultural Heritage Domain
Teodor Boyadzhiev, Gabriele Lagani, Luca Ciampi +2
The integration of computer vision and deep learning is an essential part of documenting and preserving cultural heritage, as well as improving visitor experiences. In recent years…
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…
Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training
Luca Ciampi, Gabriele Lagani, Giuseppe Amato +1
Supervised deep learning achieves strong performance in biomedical image segmentation but relies on costly pixel-wise annotations, motivating semi-supervised approaches that exploi…
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…
Biologically-inspired Semi-supervised Semantic Segmentation for Biomedical Imaging
Luca Ciampi, Gabriele Lagani, Giuseppe Amato +1
We propose a novel bio-inspired semi-supervised learning approach for training downsampling-upsampling semantic segmentation architectures. The first stage does not use backpropaga…
Mind the Prompt: A Novel Benchmark for Prompt-based Class-Agnostic Counting
Luca Ciampi, Nicola Messina, Matteo Pierucci +3
Recently, object counting has shifted towards class-agnostic counting (CAC), which counts instances of arbitrary object classes never seen during model training. With advancements…
Development of a Realistic Crowd Simulation Environment for Fine-grained Validation of People Tracking Methods
Paweł Foszner, Agnieszka Szczęsna, Luca Ciampi +7
Generally, crowd datasets can be collected or generated from real or synthetic sources. Real data is generated by using infrastructure-based sensors (such as static cameras or othe…
CrowdSim2: an Open Synthetic Benchmark for Object Detectors
Paweł Foszner, Agnieszka Szczęsna, Luca Ciampi +7
Data scarcity has become one of the main obstacles to developing supervised models based on Artificial Intelligence in Computer Vision. Indeed, Deep Learning-based models systemati…
A Spatio-Temporal Attentive Network for Video-Based Crowd Counting
Marco Avvenuti, Marco Bongiovanni, Luca Ciampi +3
Automatic people counting from images has recently drawn attention for urban monitoring in modern Smart Cities due to the ubiquity of surveillance camera networks. Current computer…
Deep Learning Techniques for Visual Counting
Luca Ciampi
In this dissertation, we investigated and enhanced Deep Learning (DL) techniques for counting objects, like pedestrians, cells or vehicles, in still images or video frames. In part…
MOBDrone: a Drone Video Dataset for Man OverBoard Rescue
Donato Cafarelli, Luca Ciampi, Lucia Vadicamo +6
Modern Unmanned Aerial Vehicles (UAV) equipped with cameras can play an essential role in speeding up the identification and rescue of people who have fallen overboard, i.e., man o…
Multi-Camera Vehicle Counting Using Edge-AI
Luca Ciampi, Claudio Gennaro, Fabio Carrara +3
This paper presents a novel solution to automatically count vehicles in a parking lot using images captured by smart cameras. Unlike most of the literature on this task, which focu…
Unsupervised Vehicle Counting via Multiple Camera Domain Adaptation
Luca Ciampi, Carlos Santiago, Joao Paulo Costeira +2
Monitoring vehicle flows in cities is crucial to improve the urban environment and quality of life of citizens. Images are the best sensing modality to perceive and assess the flow…
Virtual to Real adaptation of Pedestrian Detectors
Luca Ciampi, Nicola Messina, Fabrizio Falchi +2
Pedestrian detection through Computer Vision is a building block for a multitude of applications. Recently, there was an increasing interest in Convolutional Neural Network-based a…