Zobrazeno 1 - 10
of 383
pro vyhledávání: '"Farinella, Giovanni Maria"'
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
In this paper, we address the challenge of unsupervised mistake detection in egocentric procedural video through the analysis of gaze signals. Traditional supervised mistake detection methods rely on manually labeled mistakes, and hence suffer from d
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
In this study, we investigate the effectiveness of synthetic data in enhancing egocentric hand-object interaction detection. Via extensive experiments and comparative analyses on three egocentric datasets, VISOR, EgoHOS, and ENIGMA-51, our findings r
Externí odkaz:
http://arxiv.org/abs/2312.02672
Autor:
Quattrocchi, Camillo, Furnari, Antonino, Di Mauro, Daniele, Giuffrida, Mario Valerio, Farinella, Giovanni Maria
We consider the problem of transferring a temporal action segmentation system initially designed for exocentric (fixed) cameras to an egocentric scenario, where wearable cameras capture video data. The conventional supervised approach requires the co
Externí odkaz:
http://arxiv.org/abs/2312.02638
Autor:
Grauman, Kristen, Westbury, Andrew, Torresani, Lorenzo, Kitani, Kris, Malik, Jitendra, Afouras, Triantafyllos, Ashutosh, Kumar, Baiyya, Vijay, Bansal, Siddhant, Boote, Bikram, Byrne, Eugene, Chavis, Zach, Chen, Joya, Cheng, Feng, Chu, Fu-Jen, Crane, Sean, Dasgupta, Avijit, Dong, Jing, Escobar, Maria, Forigua, Cristhian, Gebreselasie, Abrham, Haresh, Sanjay, Huang, Jing, Islam, Md Mohaiminul, Jain, Suyog, Khirodkar, Rawal, Kukreja, Devansh, Liang, Kevin J, Liu, Jia-Wei, Majumder, Sagnik, Mao, Yongsen, Martin, Miguel, Mavroudi, Effrosyni, Nagarajan, Tushar, Ragusa, Francesco, Ramakrishnan, Santhosh Kumar, Seminara, Luigi, Somayazulu, Arjun, Song, Yale, Su, Shan, Xue, Zihui, Zhang, Edward, Zhang, Jinxu, Castillo, Angela, Chen, Changan, Fu, Xinzhu, Furuta, Ryosuke, Gonzalez, Cristina, Gupta, Prince, Hu, Jiabo, Huang, Yifei, Huang, Yiming, Khoo, Weslie, Kumar, Anush, Kuo, Robert, Lakhavani, Sach, Liu, Miao, Luo, Mi, Luo, Zhengyi, Meredith, Brighid, Miller, Austin, Oguntola, Oluwatumininu, Pan, Xiaqing, Peng, Penny, Pramanick, Shraman, Ramazanova, Merey, Ryan, Fiona, Shan, Wei, Somasundaram, Kiran, Song, Chenan, Southerland, Audrey, Tateno, Masatoshi, Wang, Huiyu, Wang, Yuchen, Yagi, Takuma, Yan, Mingfei, Yang, Xitong, Yu, Zecheng, Zha, Shengxin Cindy, Zhao, Chen, Zhao, Ziwei, Zhu, Zhifan, Zhuo, Jeff, Arbelaez, Pablo, Bertasius, Gedas, Crandall, David, Damen, Dima, Engel, Jakob, Farinella, Giovanni Maria, Furnari, Antonino, Ghanem, Bernard, Hoffman, Judy, Jawahar, C. V., Newcombe, Richard, Park, Hyun Soo, Rehg, James M., Sato, Yoichi, Savva, Manolis, Shi, Jianbo, Shou, Mike Zheng, Wray, Michael
We present Ego-Exo4D, a diverse, large-scale multimodal multiview video dataset and benchmark challenge. Ego-Exo4D centers around simultaneously-captured egocentric and exocentric video of skilled human activities (e.g., sports, music, dance, bike re
Externí odkaz:
http://arxiv.org/abs/2311.18259