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X-WR-CALDESC:Events for ITSERR
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DTSTART;VALUE=DATE:20240929
DTEND;VALUE=DATE:20240930
DTSTAMP:20260416T030646
CREATED:20250310T112359Z
LAST-MODIFIED:20250310T144344Z
UID:1244-1727568000-1727654399@www.itserr.it
SUMMARY:(AI4DH) 3rd International Workshop on Artificial Intelligence for Digital Humanities
DESCRIPTION:Our colleague\, Giuseppe Cartella (University of Modena and Reggio Emilia) participated in the 3rd International Workshop on Artificial Intelligence for Digital Humanities (AI4DH)\, presenting the paper “Pixels of Faith: Exploiting Visual Saliency to Detect Religious Image Manipulation.” \nCo-authored with Vittorio Cuculo\, Marcella Cornia (University of Modena and Reggio Emilia)\, Marco Papasidero (University of Palermo)\, Federico Ruozzi and Rita Cucchiara (University of Modena and Reggio Emilia)\, the study addresses the challenge of detecting partially manipulated religious images using deep learning models enhanced by human-derived visual saliency maps. The research highlights a human-in-the-loop approach to improve fake detection systems\, contributing to the preservation of religious and cultural content in the age of generative AI.
URL:https://www.itserr.it/event/ai4dh-3rd-international-workshop-on-artificial-intelligence-for-digital-humanities/
LOCATION:MiCo\, Milan\, Italy
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BEGIN:VEVENT
DTSTART;VALUE=DATE:20240929
DTEND;VALUE=DATE:20241005
DTSTAMP:20260416T030646
CREATED:20250310T122018Z
LAST-MODIFIED:20250310T144140Z
UID:1251-1727568000-1728086399@www.itserr.it
SUMMARY:Computer Vision - ECCV2024 18th European Conference
DESCRIPTION:Federico Cocchi and Marcella Cornia (University of Modena and Reggio Emilia) participated in the 18th European Conference on Computer Vision (ECCV 2024)\, presenting the paper “Safe-CLIP: Removing NSFW Concepts from Vision-and-Language Models.” \nThe study addresses the challenge of enhancing the safety of vision-and-language models\, such as CLIP\, by reducing their sensitivity to NSFW (not safe for work) inputs. The proposed approach “unlearns” unsafe concepts by fine-tuning the model on synthetically generated safe and unsafe data\, improving its reliability in sensitive and trustworthy contexts. The research demonstrates how this method can be effectively integrated with pre-trained generative models for safer applications.
URL:https://www.itserr.it/event/computer-vision-eccv2024-18th-european-conference/
LOCATION:MiCo\, Milan\, Italy
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