2 citations · 6 across the 45 of their papers we have counts for
52 papers
ShieldCLIP: Selective Safety Alignment for Harmful Content Mitigation in Multimodal Foundation Models
Tobia Poppi, Silvia Cappelletti, Samuele Poppi +4
Multimodal encoders such as CLIP underlie many downstream systems, but their web-scale training data embed harmful associations that safety alignment must suppress without unnecess…
Editing Everything Everywhere All at Once
Fabio Quattrini, Carmine Zaccagnino, Enis Simsar +4
Editing multiple elements of an image in a single forward pass is a practical alternative to multi-turn image manipulation, offering improved efficiency and potentially better harm…
Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation
Tobia Poppi, Silvia Cappelletti, Sara Sarto +5
Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring retraining or tailored interven…
A Text Recognition Dataset from Sahidic Coptic Ancient Manuscripts
Fabio Quattrini, Carmine Zaccagnino, Costanza Bianchi +2
In this work, we target Handwritten Text Recognition (HTR) in low-resource scenarios, which arise from underrepresented languages, rare scripts, and degraded visual conditions typi…
Hallucination Early Detection in Diffusion Models
Federico Betti, Lorenzo Baraldi, Rita Cucchiara +1
Text-to-Image generation has seen significant advancements in output realism with the advent of diffusion models. However, diffusion models encounter difficulties when tasked with…
RaTA-Tool: Retrieval-based Tool Selection with Multimodal Large Language Models
Gabriele Mattioli, Evelyn Turri, Sara Sarto +3
Tool learning with foundation models aims to endow AI systems with the ability to invoke external resources -- such as APIs, computational utilities, and specialized models -- to s…
Zero-Shot Synthetic-to-Real Handwritten Text Recognition via Task Analogies
Carlos Garrido-Munoz, Aniello Panariello, Silvia Cascianelli +4
Handwritten Text Recognition (HTR) models trained on synthetic handwriting often struggle to generalize to real text, and existing adaptation methods still require real samples fro…
Look Twice: Training-Free Evidence Highlighting for Knowledge-based Visual Question Answering
Marco Morini, Sara Sarto, Marcella Cornia +2
Knowledge-based Visual Question Answering (KB-VQA) requires Multimodal Large Language Models (MLLMs) to identify and combine fine-grained visual cues with retrieved textual evidenc…
Tiny Inference-Time Scaling with Latent Verifiers
Davide Bucciarelli, Evelyn Turri, Lorenzo Baraldi +2
Inference-time scaling has emerged as an effective way to improve generative models at test time by using a verifier to score and select candidate outputs. A common choice is to em…
Dress-ED: Instruction-Guided Editing for Virtual Try-On and Try-Off
Davide Lobba, Fulvio Sanguigni, Bin Ren +3
Recent advances in Virtual Try-On (VTON) and Virtual Try-Off (VTOFF) have greatly improved photo-realistic fashion synthesis and garment reconstruction. However, existing datasets…
Shifting the Breaking Point of Flow Matching for Multi-Instance Editing
Carmine Zaccagnino, Fabio Quattrini, Enis Simsar +4
Flow matching models have recently emerged as an efficient alternative to diffusion, especially for text-guided image generation and editing, offering faster inference through cont…
A Unified Masked Jigsaw Puzzle Framework for Vision and Language Models
Weixin Ye, Wei Wang, Yahui Liu +5
In federated learning, Transformer, as a popular architecture, faces critical challenges in defending against gradient attacks and improving model performance in both Computer Visi…
CounterVid: Counterfactual Video Generation for Mitigating Action and Temporal Hallucinations in Video-Language Models
Tobia Poppi, Burak Uzkent, Amanmeet Garg +7
Video-language models (VLMs) achieve strong multimodal understanding but remain prone to hallucinations, especially when reasoning about actions and temporal order. Existing mitiga…
Seeing Beyond Words: Self-Supervised Visual Learning for Multimodal Large Language Models
Davide Caffagni, Sara Sarto, Marcella Cornia +5
Multimodal Large Language Models (MLLMs) have recently demonstrated impressive capabilities in connecting vision and language, yet their proficiency in fundamental visual reasoning…
Autoregressive Styled Text Image Generation, but Make it Reliable
Carmine Zaccagnino, Fabio Quattrini, Vittorio Pippi +3
Generating faithful and readable styled text images (especially for Styled Handwritten Text generation - HTG) is an open problem with several possible applications across graphic d…
ReAG: Reasoning-Augmented Generation for Knowledge-based Visual Question Answering
Alberto Compagnoni, Marco Morini, Sara Sarto +5
Multimodal Large Language Models (MLLMs) have shown impressive capabilities in jointly understanding text, images, and videos, often evaluated via Visual Question Answering (VQA).…
Recurrence Meets Transformers for Universal Multimodal Retrieval
Davide Caffagni, Sara Sarto, Marcella Cornia +2
With the rapid advancement of multimodal retrieval and its application in LLMs and multimodal LLMs, increasingly complex retrieval tasks have emerged. Existing methods predominantl…
Mitigating Hallucinations in Multimodal LLMs via Object-aware Preference Optimization
Alberto Compagnoni, Davide Caffagni, Nicholas Moratelli +3
Multimodal Large Language Models (MLLMs) emerge as a unified interface to address a multitude of tasks, ranging from NLP to computer vision. Despite showcasing state-of-the-art res…
Dual Orthogonal Guidance for Robust Diffusion-based Handwritten Text Generation
Konstantina Nikolaidou, George Retsinas, Giorgos Sfikas +3
Diffusion-based Handwritten Text Generation (HTG) approaches achieve impressive results on frequent, in-vocabulary words observed at training time and on regular styles. However, t…
Quo Vadis Handwritten Text Generation for Handwritten Text Recognition?
Vittorio Pippi, Konstantina Nikolaidou, Silvia Cascianelli +4
The digitization of historical manuscripts presents significant challenges for Handwritten Text Recognition (HTR) systems, particularly when dealing with small, author-specific col…
Modeling Human Gaze Behavior with Diffusion Models for Unified Scanpath Prediction
Giuseppe Cartella, Vittorio Cuculo, Alessandro D'Amelio +3
Predicting human gaze scanpaths is crucial for understanding visual attention, with applications in human-computer interaction, autonomous systems, and cognitive robotics. While de…
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
Hicham Eddoubi, Jonas Ricker, Federico Cocchi +7
AI-generated images have reached a quality level at which humans are incapable of reliably distinguishing them from real images. To counteract the inherent risk of fraud and disinf…
TUNA: Comprehensive Fine-grained Temporal Understanding Evaluation on Dense Dynamic Videos
Fanheng Kong, Jingyuan Zhang, Hongzhi Zhang +7
Videos are unique in their integration of temporal elements, including camera, scene, action, and attribute, along with their dynamic relationships over time. However, existing ben…
What Changed? Detecting and Evaluating Instruction-Guided Image Edits with Multimodal Large Language Models
Lorenzo Baraldi, Davide Bucciarelli, Federico Betti +3
Instruction-based image editing models offer increased personalization opportunities in generative tasks. However, properly evaluating their results is challenging, and most of the…