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20232026
most citedDelving into CLIP latent space for Video Anomaly Recognition

1 citations · 1 across the 6 of their papers we have counts for

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cs.CV2026

Spot-the-shift: Evaluating Grounded Image Difference Captioning of Long-term Changes

Benedetta Liberatori, Nermin Samet, Paolo Rota +4

Long-term change understanding from images of the same place revisited over time is a challenging task with applications in map maintenance and urban infrastructure monitoring. Pri…

cs.CV2026

Zero-Shot Temporal Action Localization Through Textual Guidance

Benedetta Liberatori, Alessandro Conti, Lorenzo Vaquero +3

Zero-shot temporal action localization (ZS-TAL) consists of classifying and localizing actions in untrimmed videos, where action classes are unseen at training time. Existing work…

cs.CV2025

Dense Motion Captioning

Shiyao Xu, Benedetta Liberatori, Gül Varol +1

Recent advances in 3D human motion and language integration have primarily focused on text-to-motion generation, leaving the task of motion understanding relatively unexplored. We…

cs.CV2025

ConViS-Bench: Estimating Video Similarity Through Semantic Concepts

Benedetta Liberatori, Alessandro Conti, Lorenzo Vaquero +3

What does it mean for two videos to be similar? Videos may appear similar when judged by the actions they depict, yet entirely different if evaluated based on the locations where t…

cs.CV2024

Conditioned Prompt-Optimization for Continual Deepfake Detection

Francesco Laiti, Benedetta Liberatori, Thomas De Min +1

The rapid advancement of generative models has significantly enhanced the realism and customization of digital content creation. The increasing power of these tools, coupled with t…

cs.CV2024

Test-Time Zero-Shot Temporal Action Localization

Benedetta Liberatori, Alessandro Conti, Paolo Rota +2

Zero-Shot Temporal Action Localization (ZS-TAL) seeks to identify and locate actions in untrimmed videos unseen during training. Existing ZS-TAL methods involve fine-tuning a model…