Zobrazeno 1 - 10
of 991
pro vyhledávání: '"Farinella, P"'
Autor:
Manigrasso, Zaira, Dunnhofer, Matteo, Furnari, Antonino, Nottebaum, Moritz, Finocchiaro, Antonio, Marana, Davide, Farinella, Giovanni Maria, Micheloni, Christian
Episodic memory retrieval aims to enable wearable devices with the ability to recollect from past video observations objects or events that have been observed (e.g., "where did I last see my smartphone?"). Despite the clear relevance of the task for
Externí odkaz:
http://arxiv.org/abs/2411.16934
Unsupervised domain adaptation remains a critical challenge in enabling the knowledge transfer of models across unseen domains. Existing methods struggle to balance the need for domain-invariant representations with preserving domain-specific feature
Externí odkaz:
http://arxiv.org/abs/2411.15557
Autor:
Plini, Leonardo, Scofano, Luca, De Matteis, Edoardo, di Melendugno, Guido Maria D'Amely, Flaborea, Alessandro, Sanchietti, Andrea, Farinella, Giovanni Maria, Galasso, Fabio, Furnari, Antonino
Identifying procedural errors online from egocentric videos is a critical yet challenging task across various domains, including manufacturing, healthcare, and skill-based training. The nature of such mistakes is inherently open-set, as unforeseen or
Externí odkaz:
http://arxiv.org/abs/2411.02570
Autor:
Mur-Labadia, Lorenzo, Martinez-Cantin, Ruben, Guerrero-Campo, Josechu, Farinella, Giovanni Maria
Short-Term object-interaction Anticipation (STA) consists of detecting the location of the next-active objects, the noun and verb categories of the interaction, and the time to contact from the observation of egocentric video. We propose STAformer, a
Externí odkaz:
http://arxiv.org/abs/2407.04369
We address the challenge of unsupervised mistake detection in egocentric video of skilled human activities through the analysis of gaze signals. While traditional methods rely on manually labeled mistakes, our approach does not require mistake annota
Externí odkaz:
http://arxiv.org/abs/2406.08379
Procedural activities are sequences of key-steps aimed at achieving specific goals. They are crucial to build intelligent agents able to assist users effectively. In this context, task graphs have emerged as a human-understandable representation of p
Externí odkaz:
http://arxiv.org/abs/2406.01486
Autor:
Mur-Labadia, Lorenzo, Martinez-Cantin, Ruben, Guerrero, Josechu, Farinella, Giovanni Maria, Furnari, Antonino
Short-Term object-interaction Anticipation consists of detecting the location of the next-active objects, the noun and verb categories of the interaction, and the time to contact from the observation of egocentric video. This ability is fundamental f
Externí odkaz:
http://arxiv.org/abs/2406.01194
Autor:
Liventsev, Vadim, Kumar, Vivek, Susaiyah, Allmin Pradhap Singh, Wu, Zixiu, Rodin, Ivan, Yaar, Asfand, Balloccu, Simone, Beraziuk, Marharyta, Battiato, Sebastiano, Farinella, Giovanni Maria, Härmä, Aki, Helaoui, Rim, Petkovic, Milan, Recupero, Diego Reforgiato, Reiter, Ehud, Riboni, Daniele, Sterling, Raymond
The use of machine learning in Healthcare has the potential to improve patient outcomes as well as broaden the reach and affordability of Healthcare. The history of other application areas indicates that strong benchmarks are essential for the develo
Externí odkaz:
http://arxiv.org/abs/2405.02770
Autor:
Flaborea, Alessandro, di Melendugno, Guido Maria D'Amely, Plini, Leonardo, Scofano, Luca, De Matteis, Edoardo, Furnari, Antonino, Farinella, Giovanni Maria, Galasso, Fabio
Promptly identifying procedural errors from egocentric videos in an online setting is highly challenging and valuable for detecting mistakes as soon as they happen. This capability has a wide range of applications across various fields, such as manuf
Externí odkaz:
http://arxiv.org/abs/2404.01933
We present Egocentric Action Scene Graphs (EASGs), a new representation for long-form understanding of egocentric videos. EASGs extend standard manually-annotated representations of egocentric videos, such as verb-noun action labels, by providing a t
Externí odkaz:
http://arxiv.org/abs/2312.03391