Giacomo D'Amicantonio — accepted AI-conference papers

2026

Scaling Self-Supervised and Cross-Modal Pretraining for Volumetric CT Transformers

Cris Claessens, Christiaan Viviers, Giacomo D'Amicantonio, Egor Bondarev, Fons van der Sommen

CVPR 2026

We introduce SPECTRE, a fully transformer-based foundation model for volumetric computed tomography (CT). Our Self-Supervised & Cross-Modal Pretraining for CT Representation Extraction (SPECTRE) approach utilizes scalable 3D Vision Transformer architectures and modern self-supervised and vision-lang

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2025

Just Dance with pi! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection

Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva, Quan Kong, Lorenzo Garattoni, Gianpiero Francesca +2

CVPR 2025highlight

Weakly-supervised methods for video anomaly detection (VAD) are conventionally based merely on RGB spatio-temporal features, which continues to limit their reliability in real-world scenarios. This is due to the fact that RGB-features are not sufficiently distinctive in setting apart categories such…

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