76 citations · 109 across the 24 of their papers we have counts for
5 papers · 1 filter
Scaling Self-Supervised and Cross-Modal Pretraining for Volumetric CT Transformers
Cris Claessens, Christiaan Viviers, Giacomo D'Amicantonio +2
We introduce SPECTRE, a fully transformer-based foundation model for volumetric computed tomography (CT). Our Self-Supervised & Cross-Modal Pretraining for CT Representation Extrac…
Mixture of Experts Guided by Gaussian Splatters Matters: A new Approach to Weakly-Supervised Video Anomaly Detection
Giacomo D'Amicantonio, Snehashis Majhi, Quan Kong +4
Video Anomaly Detection (VAD) is a challenging task due to the variability of anomalous events and the limited availability of labeled data. Under the Weakly-Supervised VAD (WSVAD)…
Unmasking Performance Gaps: A Comparative Study of Human Anonymization and Its Effects on Video Anomaly Detection
Sara Abdulaziz, Egor Bondarev
Advancements in deep learning have improved anomaly detection in surveillance videos, yet they raise urgent privacy concerns due to the collection of sensitive human data. In this…
Evaluation of Human Visual Privacy Protection: A Three-Dimensional Framework and Benchmark Dataset
Sara Abdulaziz, Giacomo D'Amicantonio, Egor Bondarev
Recent advances in AI-powered surveillance have intensified concerns over the collection and processing of sensitive personal data. In response, research has increasingly focused o…
Just Dance with ! A Poly-modal Inductor for Weakly-supervised Video Anomaly Detection
Snehashis Majhi, Giacomo D'Amicantonio, Antitza Dantcheva +5
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-worl…