Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Garattoni, Lorenzo"'
Autor:
Kegeleirs, Miquel, Ramos, David Garzón, Herranz, Guillermo Legarda, Gharbi, Ilyes, Szpirer, Jeanne, Debeir, Olivier, Hasselmann, Ken, Garattoni, Lorenzo, Francesca, Gianpiero, Birattari, Mauro
Swarm perception refers to the ability of a robot swarm to utilize the perception capabilities of each individual robot, forming a collective understanding of the environment. Their distributed nature enables robot swarms to continuously monitor dyna
Externí odkaz:
http://arxiv.org/abs/2410.06720
Autor:
Simoni, Alessandro, Marchetti, Francesco, Borghi, Guido, Becattini, Federico, Davoli, Davide, Garattoni, Lorenzo, Francesca, Gianpiero, Seidenari, Lorenzo, Vezzani, Roberto
Despite the recent advances in computer vision research, estimating the 3D human pose from single RGB images remains a challenging task, as multiple 3D poses can correspond to the same 2D projection on the image. In this context, depth data could hel
Externí odkaz:
http://arxiv.org/abs/2409.11104
Autor:
Kegeleirs, Miquel, Ramos, David Garzón, Herranz, Guillermo Legarda, Gharbi, Ilyes, Szpirer, Jeanne, Hasselmann, Ken, Garattoni, Lorenzo, Francesca, Gianpiero, Birattari, Mauro
Most studies in swarm robotics treat the swarm as an isolated system of interest. We argue that the prevailing view of swarms as self-sufficient, independent systems limits the scope of potential applications for swarm robotics. A robot swarm could a
Externí odkaz:
http://arxiv.org/abs/2405.04079
Autor:
Yang, Di, Wang, Yaohui, Dantcheva, Antitza, Kong, Quan, Garattoni, Lorenzo, Francesca, Gianpiero, Bremond, Francois
Skeleton-based action segmentation requires recognizing composable actions in untrimmed videos. Current approaches decouple this problem by first extracting local visual features from skeleton sequences and then processing them by a temporal model to
Externí odkaz:
http://arxiv.org/abs/2308.14500
Autor:
Kegeleirs, Miquel, Ramos, David Garzón, Garattoni, Lorenzo, Francesca, Gianpiero, Birattari, Mauro
Automatic off-line design is an attractive approach to implementing robot swarms. In this approach, a designer specifies a mission for the swarm, and an optimization process generates suitable control software for the individual robots through comput
Externí odkaz:
http://arxiv.org/abs/2305.16126
Autor:
Yang, Di, Wang, Yaohui, Kong, Quan, Dantcheva, Antitza, Garattoni, Lorenzo, Francesca, Gianpiero, Bremond, Francois
Self-supervised video representation learning aimed at maximizing similarity between different temporal segments of one video, in order to enforce feature persistence over time. This leads to loss of pertinent information related to temporal relation
Externí odkaz:
http://arxiv.org/abs/2305.06437
Autor:
Majhi, Snehashis, Dai, Rui, Kong, Quan, Garattoni, Lorenzo, Francesca, Gianpiero, Bremond, Francois
Video anomaly detection in surveillance systems with only video-level labels (i.e. weakly-supervised) is challenging. This is due to, (i) the complex integration of human and scene based anomalies comprising of subtle and sharp spatio-temporal cues i
Externí odkaz:
http://arxiv.org/abs/2301.07923
Autor:
Jung, HyunJun, Zhai, Guangyao, Wu, Shun-Cheng, Ruhkamp, Patrick, Schieber, Hannah, Rizzoli, Giulia, Wang, Pengyuan, Zhao, Hongcheng, Garattoni, Lorenzo, Meier, Sven, Roth, Daniel, Navab, Nassir, Busam, Benjamin
Estimating 6D object poses is a major challenge in 3D computer vision. Building on successful instance-level approaches, research is shifting towards category-level pose estimation for practical applications. Current category-level datasets, however,
Externí odkaz:
http://arxiv.org/abs/2212.10428
Autor:
Yang, Di, Wang, Yaohui, Dantcheva, Antitza, Garattoni, Lorenzo, Francesca, Gianpiero, Bremond, Francois
Current self-supervised approaches for skeleton action representation learning often focus on constrained scenarios, where videos and skeleton data are recorded in laboratory settings. When dealing with estimated skeleton data in real-world videos, s
Externí odkaz:
http://arxiv.org/abs/2209.00065
Autor:
Wang, Pengyuan, Jung, HyunJun, Li, Yitong, Shen, Siyuan, Srikanth, Rahul Parthasarathy, Garattoni, Lorenzo, Meier, Sven, Navab, Nassir, Busam, Benjamin
Object pose estimation is crucial for robotic applications and augmented reality. Beyond instance level 6D object pose estimation methods, estimating category-level pose and shape has become a promising trend. As such, a new research field needs to b
Externí odkaz:
http://arxiv.org/abs/2205.08811