Zobrazeno 1 - 10
of 27 398
pro vyhledávání: '"Manso, A."'
In the domain of multi-baseline stereo, the conventional understanding is that, in general, increasing baseline separation substantially enhances the accuracy of depth estimation. However, prevailing self-supervised depth estimation architectures pri
Externí odkaz:
http://arxiv.org/abs/2407.20437
Autor:
Jimeno, Marina Manso, Bachi, Keren, Gardner, George, Hurd, Yasmin L., Vaughan Jr., John Thomas, Geethanath, Sairam
Functional magnetic resonance imaging techniques benefit from echo-planar imaging's fast image acquisition but are susceptible to inhomogeneities in the main magnetic field, resulting in geometric distortion and signal loss artifacts in the images. T
Externí odkaz:
http://arxiv.org/abs/2402.18777
Autor:
Sekulić, Ivan, Terragni, Silvia, Guimarães, Victor, Khau, Nghia, Guedes, Bruna, Filipavicius, Modestas, Manso, André Ferreira, Mathis, Roland
In the realm of dialogue systems, user simulation techniques have emerged as a game-changer, redefining the evaluation and enhancement of task-oriented dialogue (TOD) systems. These methods are crucial for replicating real user interactions, enabling
Externí odkaz:
http://arxiv.org/abs/2402.13374
Autor:
Jimeno, Marina Manso, Ravi, Keerthi Sravan, Fung, Maggie, Vaughan, Jr., John Thomas, Geethanath, Sairam
Quality assessment, including inspecting the images for artifacts, is a critical step during MRI data acquisition to ensure data quality and downstream analysis or interpretation success. This study demonstrates a deep learning model to detect rigid
Externí odkaz:
http://arxiv.org/abs/2402.08749
Traffic congestion in urban areas presents significant challenges, and Intelligent Transportation Systems (ITS) have sought to address these via automated and adaptive controls. However, these systems often struggle to transfer simulated experiences
Externí odkaz:
http://arxiv.org/abs/2312.05031
Autor:
Singamaneni, Phani Teja, Bachiller-Burgos, Pilar, Manso, Luis J., Garrell, Anaís, Sanfeliu, Alberto, Spalanzani, Anne, Alami, Rachid
Socially aware robot navigation is gaining popularity with the increase in delivery and assistive robots. The research is further fueled by a need for socially aware navigation skills in autonomous vehicles to move safely and appropriately in spaces
Externí odkaz:
http://arxiv.org/abs/2311.06922
Autor:
Bizzotto, Andrea, Guzzetta, Giorgio, Marziano, Valentina, del Manso, Martina, Urdiales, Alberto Mateo, Petrone, Daniele, Cannone, Andrea, Sacco, Chiara, Poletti, Piero, Manica, Mattia, Zardini, Agnese, Trentini, Filippo, Fabiani, Massimo, Bella, Antonino, Riccardo, Flavia, Pezzotti, Patrizio, Ajelli, Marco, Merler, Stefano
The time varying reproduction number R is a critical variable for situational awareness during infectious disease outbreaks, but delays between infection and reporting hinder its accurate estimation in real time. We propose a nowcasting method for im
Externí odkaz:
http://arxiv.org/abs/2309.14801
Current, self-supervised depth estimation architectures rely on clear and sunny weather scenes to train deep neural networks. However, in many locations, this assumption is too strong. For example in the UK (2021), 149 days consisted of rain. For the
Externí odkaz:
http://arxiv.org/abs/2307.08357
Autor:
Francis, Anthony, Pérez-D'Arpino, Claudia, Li, Chengshu, Xia, Fei, Alahi, Alexandre, Alami, Rachid, Bera, Aniket, Biswas, Abhijat, Biswas, Joydeep, Chandra, Rohan, Chiang, Hao-Tien Lewis, Everett, Michael, Ha, Sehoon, Hart, Justin, How, Jonathan P., Karnan, Haresh, Lee, Tsang-Wei Edward, Manso, Luis J., Mirksy, Reuth, Pirk, Sören, Singamaneni, Phani Teja, Stone, Peter, Taylor, Ada V., Trautman, Peter, Tsoi, Nathan, Vázquez, Marynel, Xiao, Xuesu, Xu, Peng, Yokoyama, Naoki, Toshev, Alexander, Martín-Martín, Roberto
A major challenge to deploying robots widely is navigation in human-populated environments, commonly referred to as social robot navigation. While the field of social navigation has advanced tremendously in recent years, the fair evaluation of algori
Externí odkaz:
http://arxiv.org/abs/2306.16740
Autor:
Terragni, Silvia, Filipavicius, Modestas, Khau, Nghia, Guedes, Bruna, Manso, André, Mathis, Roland
This paper presents a novel application of large language models in user simulation for task-oriented dialog systems, specifically focusing on an in-context learning approach. By harnessing the power of these models, the proposed approach generates d
Externí odkaz:
http://arxiv.org/abs/2306.00774