Zobrazeno 1 - 10
of 32
pro vyhledávání: '"Pilzer, Andrea"'
Autor:
Franchi, Gianni, Laurent, Olivier, Leguéry, Maxence, Bursuc, Andrei, Pilzer, Andrea, Yao, Angela
Deep Neural Networks (DNNs) are powerful tools for various computer vision tasks, yet they often struggle with reliable uncertainty quantification - a critical requirement for real-world applications. Bayesian Neural Networks (BNN) are equipped for u
Externí odkaz:
http://arxiv.org/abs/2312.15297
Autor:
Yu, Xuanlong, Zuo, Yi, Wang, Zitao, Zhang, Xiaowen, Zhao, Jiaxuan, Yang, Yuting, Jiao, Licheng, Peng, Rui, Wang, Xinyi, Zhang, Junpei, Zhang, Kexin, Liu, Fang, Alcover-Couso, Roberto, SanMiguel, Juan C., Escudero-Viñolo, Marcos, Tian, Hanlin, Matsui, Kenta, Wang, Tianhao, Adan, Fahmy, Gao, Zhitong, He, Xuming, Bouniot, Quentin, Moghaddam, Hossein, Rai, Shyam Nandan, Cermelli, Fabio, Masone, Carlo, Pilzer, Andrea, Ricci, Elisa, Bursuc, Andrei, Solin, Arno, Trapp, Martin, Li, Rui, Yao, Angela, Chen, Wenlong, Simpson, Ivor, Campbell, Neill D. F., Franchi, Gianni
This paper outlines the winning solutions employed in addressing the MUAD uncertainty quantification challenge held at ICCV 2023. The challenge was centered around semantic segmentation in urban environments, with a particular focus on natural advers
Externí odkaz:
http://arxiv.org/abs/2309.15478
Autor:
Sortino, Renato, Cecconello, Thomas, DeMarco, Andrea, Fiameni, Giuseppe, Pilzer, Andrea, Hopkins, Andrew M., Magro, Daniel, Riggi, Simone, Sciacca, Eva, Ingallinera, Adriano, Bordiu, Cristobal, Bufano, Filomena, Spampinato, Concetto
Along with the nearing completion of the Square Kilometre Array (SKA), comes an increasing demand for accurate and reliable automated solutions to extract valuable information from the vast amount of data it will allow acquiring. Automated source fin
Externí odkaz:
http://arxiv.org/abs/2307.02392
The use of self-supervised pre-training has emerged as a promising approach to enhance the performance of visual tasks such as image classification. In this context, recent approaches have employed the Masked Image Modeling paradigm, which pre-trains
Externí odkaz:
http://arxiv.org/abs/2306.07346
Despite its crucial role in research experiments, code correctness is often presumed only on the basis of the perceived quality of results. This assumption comes with the risk of erroneous outcomes and potentially misleading findings. To address this
Externí odkaz:
http://arxiv.org/abs/2303.16166
Dynamic neural networks are a recent technique that promises a remedy for the increasing size of modern deep learning models by dynamically adapting their computational cost to the difficulty of the inputs. In this way, the model can adjust to a limi
Externí odkaz:
http://arxiv.org/abs/2302.06359
We introduce visual hints expansion for guiding stereo matching to improve generalization. Our work is motivated by the robustness of Visual Inertial Odometry (VIO) in computer vision and robotics, where a sparse and unevenly distributed set of featu
Externí odkaz:
http://arxiv.org/abs/2211.00392
Autor:
Roy, Subhankar, Trapp, Martin, Pilzer, Andrea, Kannala, Juho, Sebe, Nicu, Ricci, Elisa, Solin, Arno
Source-free domain adaptation (SFDA) aims to adapt a classifier to an unlabelled target data set by only using a pre-trained source model. However, the absence of the source data and the domain shift makes the predictions on the target data unreliabl
Externí odkaz:
http://arxiv.org/abs/2208.07591
The fusion of camera sensor and inertial data is a leading method for ego-motion tracking in autonomous and smart devices. State estimation techniques that rely on non-linear filtering are a strong paradigm for solving the associated information fusi
Externí odkaz:
http://arxiv.org/abs/2205.13821
Autor:
Pilzer, Andrea
In order to interact with the real world, humans need to perform several tasks such as object detection, pose estimation, motion estimation and distance estimation. These tasks are all part of scene understanding and are fundamental tasks of computer
Externí odkaz:
http://hdl.handle.net/11572/268252