| Unsupervised Adversarial Depth Estimation using Cycled Generative Networks A Pilzer, D Xu, M Puscas, E Ricci, N Sebe 2018 International Conference on 3D Vision (3DV), 587-595, 2018 | 199 | 2018 |
| Refine and distill: Exploiting cycle-inconsistency and knowledge distillation for unsupervised monocular depth estimation A Pilzer, S Lathuiliere, N Sebe, E Ricci Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019 | 158 | 2019 |
| Uncertainty-guided source-free domain adaptation S Roy, M Trapp, A Pilzer, J Kannala, N Sebe, E Ricci, A Solin European Conference on Computer Vision, 537-555, 2022 | 52 | 2022 |
| Progressive fusion for unsupervised binocular depth estimation using cycled networks A Pilzer, S Lathuilière, D Xu, MM Puscas, E Ricci, N Sebe IEEE Transactions on Pattern Analysis and Machine Intelligence 42 (10), 2380 …, 2019 | 28 | 2019 |
| Viraliency: Pooling local virality X Alameda-Pineda, A Pilzer, D Xu, N Sebe, E Ricci Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 26 | 2017 |
| Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation MM Puscas, D Xu, A Pilzer, N Sebe 2019 International Conference on 3D Vision (3DV), 18-26, 2019 | 23 | 2019 |
| Online adaptation through meta-learning for stereo depth estimation Z Zhang, S Lathuiliere, A Pilzer, N Sebe, E Ricci, J Yang arXiv preprint arXiv:1904.08462, 2019 | 16 | 2019 |
| Fixing overconfidence in dynamic neural networks L Meronen, M Trapp, A Pilzer, L Yang, A Solin Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 7 | 2024 |
| When good and reproducible results are a giant with feet of clay: The importance of software quality in nlp S Papi, M Gaido, A Pilzer, M Negri arXiv preprint arXiv:2303.16166, 2023 | 5 | 2023 |
| Expansion of Visual Hints for Improved Generalization in Stereo Matching A Pilzer, Y Hou, N Loppi, A Solin, J Kannala Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 3 | 2023 |
| Reproducibility is Nothing without Correctness: The Importance of Testing Code in NLP S Papi, M Gaido, M Negri, A Pilzer arXiv preprint arXiv:2303.16166, 2023 | 2 | 2023 |
| Learning to Mask and Permute Visual Tokens for Vision Transformer Pre-Training L Baraldi, R Amoroso, M Cornia, A Pilzer, R Cucchiara arXiv preprint arXiv:2306.07346, 2023 | 1 | 2023 |
| A Look at Improving Robustness in Visual-inertial SLAM by Moment Matching A Solin, R Li, A Pilzer FUSION 2022, 8, 2022 | 1 | 2022 |
| Real-time flood maps forecasting for dam-break scenarios with a transformer-based deep learning model M Pianforini, S Dazzi, A Pilzer, R Vacondio Journal of Hydrology 635, 131169, 2024 | | 2024 |
| A Transformer-Based Data-Driven Model for Real-Time Spatio-Temporal Flood Prediction M Pianforini, S Dazzi, A Pilzer, R Vacondio EGU General Assembly 2024, 2024 | | 2024 |
| Make Me a BNN: A Simple Strategy for Estimating Bayesian Uncertainty from Pre-trained Models G Franchi, O Laurent, M Leguéry, A Bursuc, A Pilzer, A Yao Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | | 2024 |
| RADiff: Controllable Diffusion Models for Radio Astronomical Maps Generation R Sortino, T Cecconello, A DeMarco, G Fiameni, A Pilzer, AM Hopkins, ... arXiv preprint arXiv:2307.02392, 2023 | | 2023 |
| Computer Vision in Human Analysis: From Face and Body to Clothes M Daoudi, R Vezzani, G Borghi, C Ferrari, M Cornia, F Becattini, A Pilzer Sensors 23 (12), 5378, 2023 | | 2023 |
| FloodSformer: Transformer based surrogate model for real time forecasting of inundation maps M Pianforini, S Dazzi, R Vacondio, A Pilzer Proceedings of the 4th IAHR Young Professionals Congress, 2023 | | 2023 |
| The Robust Semantic Segmentation UNCV2023 Challenge Results X Yu, Y Zuo, Z Wang, X Zhang, J Zhao, Y Yang, L Jiao, R Peng, X Wang, ... Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023 | | 2023 |
Elisa RicciUniversity of Trento & Fondazione Bruno Kessler在 unitn.it 的电子邮件经过验证
