2 citations · 3 across the 3 of their papers we have counts for
5 papers
Hierarchical Step Decomposition for Training-free Online Video Step Grounding
Long Zhou, Luca Zanella, Paolo Rota +2
Given a set of procedural steps, Video Step Grounding (VSG) aims to identify which steps occur in a video and localize when they are performed. Existing methods are either offline,…
Training-free Online Video Step Grounding
Luca Zanella, Massimiliano Mancini, Yiming Wang +2
Given a task and a set of steps composing it, Video Step Grounding (VSG) aims to detect which steps are performed in a video. Standard approaches for this task require a labeled tr…
Can Text-to-Video Generation help Video-Language Alignment?
Luca Zanella, Massimiliano Mancini, Willi Menapace +3
Recent video-language alignment models are trained on sets of videos, each with an associated positive caption and a negative caption generated by large language models. A problem…
Harnessing Large Language Models for Training-free Video Anomaly Detection
Luca Zanella, Willi Menapace, Massimiliano Mancini +2
Video anomaly detection (VAD) aims to temporally locate abnormal events in a video. Existing works mostly rely on training deep models to learn the distribution of normality with e…
Delving into CLIP latent space for Video Anomaly Recognition
Luca Zanella, Benedetta Liberatori, Willi Menapace +3
We tackle the complex problem of detecting and recognising anomalies in surveillance videos at the frame level, utilising only video-level supervision. We introduce the novel metho…