1 citations · 1 across the 11 of their papers we have counts for
13 papers
Retrieval-Augmented Visual Prompting: Guiding Foundation Models in Two-Photon Imaging
Salvatore Calcagno, Marco Finocchiaro, Giovanni Bellitto +3
Two-photon calcium imaging presents a challenging setting for foundation models: image appearance varies substantially across recordings and experimental conditions, annotations ar…
PERL: Parameter Efficient Reasoning in CLIP Latent Space
Simone Carnemolla, Salvatore Calcagno, Daniela Giordano +2
Contrastively trained vision-language models such as CLIP provide strong zero-shot transfer by aligning images and text in a shared embedding space. However, adapting these models…
OCCAM: Open-set Causal Concept explAnation and Ontology induction for black-box vision Models
Chiara Maria Russo, Simone Carnemolla, Simone Palazzo +3
Interpreting the decisions of deep image classifiers remains challenging, particularly in black-box settings where model internals are inaccessible. We introduce OCCAM, a framework…
UNBOX: Unveiling Black-box visual models with Natural-language
Simone Carnemolla, Chiara Russo, Simone Palazzo +5
Ensuring trustworthiness in open-world visual recognition requires models that are interpretable, fair, and robust to distribution shifts. Yet modern vision systems are increasingl…
DEXTER: Diffusion-Guided EXplanations with TExtual Reasoning for Vision Models
Simone Carnemolla, Matteo Pennisi, Sarinda Samarasinghe +5
Understanding and explaining the behavior of machine learning models is essential for building transparent and trustworthy AI systems. We introduce DEXTER, a data-free framework th…
SeeingSounds: Learning Audio-to-Visual Alignment via Text
Simone Carnemolla, Matteo Pennisi, Chiara Russo +3
We introduce SeeingSounds, a lightweight and modular framework for audio-to-image generation that leverages the interplay between audio, language, and vision-without requiring any…
QuantFormer: Learning to Quantize for Neural Activity Forecasting in Mouse Visual Cortex
Salvatore Calcagno, Isaak Kavasidis, Simone Palazzo +8
Understanding complex animal behaviors hinges on deciphering the neural activity patterns within brain circuits, making the ability to forecast neural activity crucial for developi…
Back to Supervision: Boosting Word Boundary Detection through Frame Classification
Simone Carnemolla, Salvatore Calcagno, Simone Palazzo +1
Speech segmentation at both word and phoneme levels is crucial for various speech processing tasks. It significantly aids in extracting meaningful units from an utterance, thus ena…
BioTrak: A Blockchain-based Platform for Food Chain Logistics Traceability
A. Spitalleri, I. Kavasidis, V. Cartelli +5
The food supply chain, following its globalization, has become very complex. Such complexities, introduce factors that influence adversely the quality of intermediate and final pro…
A baseline on continual learning methods for video action recognition
Giulia Castagnolo, Concetto Spampinato, Francesco Rundo +2
Continual learning has recently attracted attention from the research community, as it aims to solve long-standing limitations of classic supervisedly-trained models. However, most…
Hierarchical Domain-Adapted Feature Learning for Video Saliency Prediction
Giovanni Bellitto, Federica Proietto Salanitri, Simone Palazzo +3
In this work, we propose a 3D fully convolutional architecture for video saliency prediction that employs hierarchical supervision on intermediate maps (referred to as conspicuity…
Top-Down Saliency Detection Driven by Visual Classification
Francesca Murabito, Concetto Spampinato, Simone Palazzo +2
This paper presents an approach for top-down saliency detection guided by visual classification tasks. We first learn how to compute visual saliency when a specific visual task has…
Deep Learning Human Mind for Automated Visual Classification
Concetto Spampinato, Simone Palazzo, Isaak Kavasidis +3
What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the fi…