activity
20232026
most citedUncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation

1 citations · 1 across the 5 of their papers we have counts for

collaborators

7 papers

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…

cs.CL2025

Improving LLM First-Token Predictions in Multiple-Choice Question Answering via Output Prefilling

Silvia Cappelletti, Tobia Poppi, Samuele Poppi +5

Large Language Models (LLMs) are increasingly evaluated on multiple-choice question answering (MCQA) tasks using *first-token probability* (FTP), which selects the answer option wh…

cs.CV2025

Hyperbolic Safety-Aware Vision-Language Models

Tobia Poppi, Tejaswi Kasarla, Pascal Mettes +2

Addressing the retrieval of unsafe content from vision-language models such as CLIP is an important step towards real-world integration. Current efforts have relied on unlearning t…

cs.CV2023

Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models

Samuele Poppi, Tobia Poppi, Federico Cocchi +3

Large-scale vision-and-language models, such as CLIP, are typically trained on web-scale data, which can introduce inappropriate content and lead to the development of unsafe and b…

cs.CV2023★ 1 cited

Uncovering the Background-Induced bias in RGB based 6-DoF Object Pose Estimation

Elena Govi, Davide Sapienza, Carmelo Scribano +5

In recent years, there has been a growing trend of using data-driven methods in industrial settings. These kinds of methods often process video images or parts, therefore the integ…