16 citations · 39 across the 32 of their papers we have counts for
6 papers · 1 filter
Autoregressive Styled Text Image Generation, but Make it Reliable
Carmine Zaccagnino, Fabio Quattrini, Vittorio Pippi +3
Generating faithful and readable styled text images (especially for Styled Handwritten Text generation - HTG) is an open problem with several possible applications across graphic d…
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…
RefAM: Attention Magnets for Zero-Shot Referral Segmentation
Anna Kukleva, Enis Simsar, Alessio Tonioni +4
Most existing approaches to referring segmentation achieve strong performance only through fine-tuning or by composing multiple pre-trained models, often at the cost of additional…
Test-Time Visual In-Context Tuning
Jiahao Xie, Alessio Tonioni, Nathalie Rauschmayr +2
Visual in-context learning (VICL), as a new paradigm in computer vision, allows the model to rapidly adapt to various tasks with only a handful of prompts and examples. While effec…
Zero-Shot Styled Text Image Generation, but Make It Autoregressive
Vittorio Pippi, Fabio Quattrini, Silvia Cascianelli +2
Styled Handwritten Text Generation (HTG) has recently received attention from the computer vision and document analysis communities, which have developed several solutions, either…
Omnia de EgoTempo: Benchmarking Temporal Understanding of Multi-Modal LLMs in Egocentric Videos
Chiara Plizzari, Alessio Tonioni, Yongqin Xian +2
Understanding fine-grained temporal dynamics is crucial in egocentric videos, where continuous streams capture frequent, close-up interactions with objects. In this work, we bring…