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🖐🏻 Handscribe: A Gloss-Free Framework for Sign Language Translation and Gloss Sequence Generation 🤌🏻

🖐🏻 Handscribe: A Gloss-Free Framework for Sign Language Translation and Gloss Sequence Generation 🤌🏻

📰 Journal Paper
February 2026
Emanuele Colonna and Ivan Rinaldi and David Landi and Gennaro Vessio and Giovanna Castellano
Sign language translation systems traditionally rely on intermediate gloss representations to bridge the gap between visual input and written language output. However, manual gloss annotation is costl...
Sign Language Translation, SlowFast Network, Large Language Models, Accessibility Technologies
🎨 Art2Mus: Artwork-to-Music Generation via Visual Conditioning and Large-Scale Cross-Modal Alignment 🎶

🎨 Art2Mus: Artwork-to-Music Generation via Visual Conditioning and Large-Scale Cross-Modal Alignment 🎶

February 2026
Ivan Rinaldi and Matteo Mendula and Nicola Fanelli and Florence Levé and Matteo Testi and Giovanna Castellano and Gennaro Vessio
Music generation has advanced markedly through multimodal deep learning, enabling models to synthesize audio from text and, more recently, from images. However, existing image-conditioned systems suff...
Generative AI, Conditioned Music Generation, Multimodal Deep Learning
🖼️ Art2Mus: Bridging Visual Arts and Music through Cross-Modal Generation 🎵

🖼️ Art2Mus: Bridging Visual Arts and Music through Cross-Modal Generation 🎵

📓 Conference Paper
📍 ECCVW2024
September 2024
Ivan Rinaldi and Nicola Fanelli and Giovanna Castellano and Gennaro Vessio
Artificial Intelligence has transformed music creation by using generative models that respond to textual or visual prompts. Current image-to-music models are limited to basic images, unable to handle...
Generative AI, Conditioned Music Generation, Multimodal Deep Learning
Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations 🎥💿📚

Instructing and Prompting Large Language Models for Explainable Cross-domain Recommendations 🎥💿📚

📓 Conference Paper
📍 RecSys2024
October 2024
Alessandro Petruzzelli, Cataldo Musto, Lucrezia Laraspata, Ivan Rinaldi, Marco de Gemmis, Pasquale Lops, and Giovanni Semeraro
We present a method for using large language models (LLMs) to provide explainable cross-domain recommendations despite data scarcity. It involves instructing an LLM, creating personalized prompts, and...
Recommender System, Cross Domain Recommendation, Large Language Models

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