122 citations · 483 across the 70 of their papers we have counts for
80 papers · 1 filter
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…
Spike-TBR: a Noise Resilient Neuromorphic Event Representation
Gabriele Magrini, Federico Becattini, Luca Cultrera +3
Event cameras offer significant advantages over traditional frame-based sensors, including higher temporal resolution, lower latency and dynamic range. However, efficiently convert…
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…
Spatio-temporal Transformers for Action Unit Classification with Event Cameras
Luca Cultrera, Federico Becattini, Lorenzo Berlincioni +2
Face analysis has been studied from different angles to infer emotion, poses, shapes, and landmarks. Traditionally RGB cameras are used, yet for fine-grained tasks standard sensors…
Neuromorphic Drone Detection: an Event-RGB Multimodal Approach
Gabriele Magrini, Federico Becattini, Pietro Pala +2
In recent years, drone detection has quickly become a subject of extreme interest: the potential for fast-moving objects of contained dimensions to be used for malicious intents or…
Neuromorphic Facial Analysis with Cross-Modal Supervision
Federico Becattini, Luca Cultrera, Lorenzo Berlincioni +3
Traditional approaches for analyzing RGB frames are capable of providing a fine-grained understanding of a face from different angles by inferring emotions, poses, shapes, landmark…
Garment Attribute Manipulation with Multi-level Attention
Vittorio Casula, Lorenzo Berlincioni, Luca Cultrera +5
In the rapidly evolving field of online fashion shopping, the need for more personalized and interactive image retrieval systems has become paramount. Existing methods often strugg…
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…
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…
iSEARLE: Improving Textual Inversion for Zero-Shot Composed Image Retrieval
Lorenzo Agnolucci, Alberto Baldrati, Alberto Del Bimbo +1
Given a query consisting of a reference image and a relative caption, Composed Image Retrieval (CIR) aims to retrieve target images visually similar to the reference one while inco…
Interactive Garment Recommendation with User in the Loop
Federico Becattini, Xiaolin Chen, Andrea Puccia +4
Recommending fashion items often leverages rich user profiles and makes targeted suggestions based on past history and previous purchases. In this paper, we work under the assumpti…
Neuromorphic Face Analysis: a Survey
Federico Becattini, Lorenzo Berlincioni, Luca Cultrera +1
Neuromorphic sensors, also known as event cameras, are a class of imaging devices mimicking the function of biological visual systems. Unlike traditional frame-based cameras, which…
Neuromorphic Valence and Arousal Estimation
Lorenzo Berlincioni, Luca Cultrera, Federico Becattini +1
Recognizing faces and their underlying emotions is an important aspect of biometrics. In fact, estimating emotional states from faces has been tackled from several angles in the li…
Perceptual Quality Improvement in Videoconferencing using Keyframes-based GAN
Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini +1
In the latest years, videoconferencing has taken a fundamental role in interpersonal relations, both for personal and business purposes. Lossy video compression algorithms are the…
Restoration of Analog Videos Using Swin-UNet
Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini +1
In this paper, we present a system to restore analog videos of historical archives. These videos often contain severe visual degradation due to the deterioration of their tape supp…
ARNIQA: Learning Distortion Manifold for Image Quality Assessment
Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini +1
No-Reference Image Quality Assessment (NR-IQA) aims to develop methods to measure image quality in alignment with human perception without the need for a high-quality reference ima…
Reference-based Restoration of Digitized Analog Videotapes
Lorenzo Agnolucci, Leonardo Galteri, Marco Bertini +1
Analog magnetic tapes have been the main video data storage device for several decades. Videos stored on analog videotapes exhibit unique degradation patterns caused by tape aging…
Addressing Limitations of State-Aware Imitation Learning for Autonomous Driving
Luca Cultrera, Federico Becattini, Lorenzo Seidenari +2
Conditional Imitation learning is a common and effective approach to train autonomous driving agents. However, two issues limit the full potential of this approach: (i) the inertia…
FLODCAST: Flow and Depth Forecasting via Multimodal Recurrent Architectures
Andrea Ciamarra, Federico Becattini, Lorenzo Seidenari +1
Forecasting motion and spatial positions of objects is of fundamental importance, especially in safety-critical settings such as autonomous driving. In this work, we address the is…
Mapping Memes to Words for Multimodal Hateful Meme Classification
Giovanni Burbi, Alberto Baldrati, Lorenzo Agnolucci +2
Multimodal image-text memes are prevalent on the internet, serving as a unique form of communication that combines visual and textual elements to convey humor, ideas, or emotions.…
Deepfake detection by exploiting surface anomalies: the SurFake approach
Andrea Ciamarra, Roberto Caldelli, Federico Becattini +2
The ever-increasing use of synthetically generated content in different sectors of our everyday life, one for all media information, poses a strong need for deepfake detection tool…
Exploiting CLIP-based Multi-modal Approach for Artwork Classification and Retrieval
Alberto Baldrati, Marco Bertini, Tiberio Uricchio +1
Given the recent advances in multimodal image pretraining where visual models trained with semantically dense textual supervision tend to have better generalization capabilities th…
Composed Image Retrieval using Contrastive Learning and Task-oriented CLIP-based Features
Alberto Baldrati, Marco Bertini, Tiberio Uricchio +1
Given a query composed of a reference image and a relative caption, the Composed Image Retrieval goal is to retrieve images visually similar to the reference one that integrates th…
Diffusion Based Augmentation for Captioning and Retrieval in Cultural Heritage
Dario Cioni, Lorenzo Berlincioni, Federico Becattini +1
Cultural heritage applications and advanced machine learning models are creating a fruitful synergy to provide effective and accessible ways of interacting with artworks. Smart aud…