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20242026
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cs.CV2026

ProSkill: Segment-Level Skill Assessment in Procedural Videos

Michele Mazzamuto, Daniele Di Mauro, Gianpiero Francesca +2

Skill assessment in procedural videos is crucial for the objective evaluation of human performance in settings such as manufacturing and procedural daily tasks. Current research on…

cs.CV2025

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)…

cs.CV2025

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…

cs.CV2024

Depth-based Privileged Information for Boosting 3D Human Pose Estimation on RGB

Alessandro Simoni, Francesco Marchetti, Guido Borghi +6

Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the…

cs.CV2024

LAC: Latent Action Composition for Skeleton-based Action Segmentation

Di Yang, Yaohui Wang, Antitza Dantcheva +4

Skeleton-based action segmentation requires recognizing composable actions in untrimmed videos. Current approaches decouple this problem by first extracting local visual features f…