6 citations · 15 across the 13 of their papers we have counts for
15 papers
WASP: Weakly Aligned Spatiotemporal Pairs for Fetal Brain MRI-Ultrasound Learning
Francesco Correnti, Gabriele Magrini, Marco Mistretta +6
Magnetic Resonance Imaging (MRI) is widely regarded as the optimal sensor for fetal brain analysis due to its superior soft-tissue contrast and anatomical detail. However, its high…
Spherical Interpolation for Backward-Compatible Multimodal Representations
Simone Ricci, Niccolò Biondi, Federico Pernici
Contrastive vision-language models map visual and textual representations into a shared normalized embedding space, making cosine similarity the natural metric for cross-modal retr…
DistMoE: Private-data Rehearsal-free Routing in Mixture-of-Experts for Distributed Instruction Tuning
Mainak Singha, Niccolò Biondi, Elisa Ricci +1
Multimodal Large Language Models (MLLMs) have shown strong multimodal instruction-following ability, but adapting them to diverse visual-language domains typically assumes centrali…
A Stationary (and Therefore Compatible) Representation is All You Need
Niccolò Biondi, Federico Pernici, Simone Ricci +1
Learning compatible representations aims to learn feature representations that can be used interchangeably over time whenever a model undergoes updates. In this paper, we demonstra…
PEPR: Privileged Event-based Predictive Regularization for Domain Generalization
Gabriele Magrini, Federico Becattini, Niccolò Biondi +1
Deep neural networks for visual perception are highly susceptible to domain shift, which poses a critical challenge for real-world deployment under conditions that differ from the…
Mitigating Negative Flips via Margin Preserving Training
Simone Ricci, Niccolò Biondi, Federico Pernici +1
Minimizing inconsistencies across successive versions of an AI system is as crucial as reducing the overall error. In image classification, such inconsistencies manifest as negativ…
-Orthogonality Regularization for Compatible Representation Learning
Simone Ricci, Niccolò Biondi, Federico Pernici +2
Retrieval systems rely on representations learned by increasingly powerful models. However, due to the high training cost and inconsistencies in learned representations, there is s…
FRED: The Florence RGB-Event Drone Dataset
Gabriele Magrini, Niccolò Marini, Federico Becattini +4
Small, fast, and lightweight drones present significant challenges for traditional RGB cameras due to their limitations in capturing fast-moving objects, especially under challengi…
ComiCap: A VLMs pipeline for dense captioning of Comic Panels
Emanuele Vivoli, Niccolò Biondi, Marco Bertini +1
The comic domain is rapidly advancing with the development of single- and multi-page analysis and synthesis models. Recent benchmarks and datasets have been introduced to support a…
Backward-Compatible Aligned Representations via an Orthogonal Transformation Layer
Simone Ricci, Niccolò Biondi, Federico Pernici +1
Visual retrieval systems face significant challenges when updating models with improved representations due to misalignment between the old and new representations. The costly and…
Comics Datasets Framework: Mix of Comics datasets for detection benchmarking
Emanuele Vivoli, Irene Campaioli, Mariateresa Nardoni +3
Comics, as a medium, uniquely combine text and images in styles often distinct from real-world visuals. For the past three decades, computational research on comics has evolved fro…
Stationary Representations: Optimally Approximating Compatibility and Implications for Improved Model Replacements
Niccolò Biondi, Federico Pernici, Simone Ricci +1
Learning compatible representations enables the interchangeable use of semantic features as models are updated over time. This is particularly relevant in search and retrieval syst…
CL2R: Compatible Lifelong Learning Representations
Niccolo Biondi, Federico Pernici, Matteo Bruni +2
In this paper, we propose a method to partially mimic natural intelligence for the problem of lifelong learning representations that are compatible. We take the perspective of a le…
Contrastive Supervised Distillation for Continual Representation Learning
Tommaso Barletti, Niccolo' Biondi, Federico Pernici +2
In this paper, we propose a novel training procedure for the continual representation learning problem in which a neural network model is sequentially learned to alleviate catastro…
CoReS: Compatible Representations via Stationarity
Niccolo Biondi, Federico Pernici, Matteo Bruni +1
Compatible features enable the direct comparison of old and new learned features allowing to use them interchangeably over time. In visual search systems, this eliminates the need…