28 citations · 28 across the 15 of their papers we have counts for
18 papers
Dr. No: Feature Steering for Abstention in Vision-Language Models
Farid Hasanov, Riccardo Renzulli, Bahram Zonooz +2
Large foundation models often answer even when the available visual or textual evidence is insufficient to support a reliable conclusion. This failure to abstain makes them hard to…
TORINO: Token Reduction via Interpretable Concept Overlap in Vision-Language Models
Riccardo Renzulli, Gabriele Spadaro, Shruthi Gowda +2
Vision-Language Models (VLMs) have demonstrated impressive capabilities across different tasks, but their computational cost is dominated by the large number of visual tokens fed t…
Look But Don't Touch with Sparse Autoencoders for Unlearning in Diffusion Models
Enrico Cassano, Riccardo Renzulli, Rayyan Ahmed +2
Sparse autoencoders (SAEs) have recently been proposed as interpretable tools for concept-level manipulation, under the assumption that isolated features can serve as controllable…
Cardiac Output Prediction from Echocardiograms: Self-Supervised Learning with Limited Data
Adson Duarte, Davide Vitturini, Emanuele Milillo +9
Cardiac Output (CO) is a key parameter in the diagnosis and management of cardiovascular diseases. However, its accurate measurement requires right-heart catheterization, an invasi…
Synthetic Dataset Generation and Validation for Robotic Surgery Instrument Segmentation
Giorgio Chiesa, Rossella Borra, Vittorio Lauro +5
This paper presents a comprehensive workflow for generating and validating a synthetic dataset designed for robotic surgery instrument segmentation. A 3D reconstruction of the Da V…
Automated Prediction of Paravalvular Regurgitation before Transcatheter Aortic Valve Implantation
Michele Cannito, Riccardo Renzulli, Adson Duarte +9
Severe aortic stenosis is a common and life-threatening condition in elderly patients, often treated with Transcatheter Aortic Valve Implantation (TAVI). Despite procedural advance…
MedSAE: Dissecting MedCLIP Representations with Sparse Autoencoders
Riccardo Renzulli, Colas Lepoutre, Enrico Cassano +1
Artificial intelligence in healthcare requires models that are accurate and interpretable. We advance mechanistic interpretability in medical vision by applying Medical Sparse Auto…
SAEmnesia: Erasing Concepts in Diffusion Models with Supervised Sparse Autoencoders
Enrico Cassano, Riccardo Renzulli, Marco Nurisso +3
Concept unlearning in diffusion models is hampered by feature splitting, where concepts are distributed across many latent features, making their removal challenging and computatio…
When Does Pruning Benefit Vision Representations?
Enrico Cassano, Riccardo Renzulli, Andrea Bragagnolo +1
Pruning is widely used to reduce the complexity of deep learning models, but its effects on interpretability and representation learning remain poorly understood. This paper invest…
GoDe: Gaussians on Demand for Backbone-Agnostic Progressive Compression
Francesco Di Sario, Riccardo Renzulli, Marco Grangetto +2
Splat-based radiance fields have rapidly diversified in primitive type, kernel support, and appearance parameterization. This diversity makes compression difficult: most codecs are…
Boost Your NeRF: A Model-Agnostic Mixture of Experts Framework for High Quality and Efficient Rendering
Francesco Di Sario, Riccardo Renzulli, Enzo Tartaglione +1
Since the introduction of NeRFs, considerable attention has been focused on improving their training and inference times, leading to the development of Fast-NeRFs models. Despite d…
Unsupervised contrastive analysis for anomaly detection in brain MRIs via conditional diffusion models
Cristiano Patrício, Carlo Alberto Barbano, Attilio Fiandrotti +4
Contrastive Analysis (CA) detects anomalies by contrasting patterns unique to a target group (e.g., unhealthy subjects) from those in a background group (e.g., healthy subjects). I…
Hierarchical Object-Centric Learning with Capsule Networks
Riccardo Renzulli
Capsule networks (CapsNets) were introduced to address convolutional neural networks limitations, learning object-centric representations that are more robust, pose-aware, and inte…
AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation
Carlo Alberto Barbano, Riccardo Renzulli, Marco Grosso +3
In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a st…
LSVL: Large-scale season-invariant visual localization for UAVs
Jouko Kinnari, Riccardo Renzulli, Francesco Verdoja +1
Localization of autonomous unmanned aerial vehicles (UAVs) relies heavily on Global Navigation Satellite Systems (GNSS), which are susceptible to interference. Especially in securi…
Towards Efficient Capsule Networks
Riccardo Renzulli, Marco Grangetto
From the moment Neural Networks dominated the scene for image processing, the computational complexity needed to solve the targeted tasks skyrocketed: against such an unsustainable…
Lung nodules segmentation from CT with DeepHealth toolkit
Hafiza Ayesha Hoor Chaudhry, Riccardo Renzulli, Daniele Perlo +7
The accurate and consistent border segmentation plays an important role in the tumor volume estimation and its treatment in the field of Medical Image Segmentation. Globally, Lung…
Capsule Networks Do Not Need to Model Everything
Riccardo Renzulli, Enzo Tartaglione, Marco Grangetto
Capsule networks are biologically inspired neural networks that group neurons into vectors called capsules, each explicitly representing an object or one of its parts. The routing…