Riccardo Volpi
Riccardo Volpi
Naver Labs Europe
Bestätigte E-Mail-Adresse bei - Startseite
Zitiert von
Zitiert von
Certifying Some Distributional Robustness with Principled Adversarial Training
A Sinha, H Namkoong, R Volpi, J Duchi
arXiv:1710.10571v5 [stat.ML], 2020
Generalizing to Unseen Domains via Adversarial Data Augmentation
R Volpi, H Namkoong, O Sener, J Duchi, V Murino, S Savarese
Advances in Neural Information Processing Systems (NeurIPS), 2018
Adversarial Feature Augmentation for Unsupervised Domain Adaptation
R Volpi, P Morerio, S Savarese, V Murino
Computer Vision and Pattern Recognition (CVPR), 2018
Curriculum Dropout
P Morerio, J Cavazza, R Volpi, R Vidal, V Murino
International Conference on Computer Vision (ICCV), 2017
Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets
R Volpi, V Murino
International Conference on Computer Vision (ICCV), 2019
Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning
R Volpi, D Larlus, G Rogez
Computer Vision and Pattern Recognition (CVPR), 2021
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
P Morerio, R Volpi, R Ragonesi, V Murino
Winter Conference on Applications of Computer Vision (WACV), 2020
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey
G Csurka, R Volpi, B Chidlovskii
arXiv:2112.03241 [cs.CV], 2021
Explainable Deep Classification Models for Domain Generalization
A Zunino, SA Bargal, R Volpi, M Sameki, J Zhang, S Sclaroff, V Murino, ...
IEEE CVPR Workshop on Fair, Data Efficient and Trusted Computer Vision, 2021
Make Some Noise: Reliable and Efficient Single-Step Adversarial Training
P de Jorge, A Bibi, R Volpi, A Sanyal, PHS Torr, G Rogez, PK Dokania
Advances in Neural Information Processing Systems (NeurIPS), 2022
Learning Unbiased Representations via Mutual Information Backpropagation
R Ragonesi, R Volpi, J Cavazza, V Murino
Learning from Limited and Imperfect Data (L2ID) Workshop at CVPR, 2021
Predicting Intentions from Motion: The Subject-Adversarial Adaptation Approach
A Zunino, J Cavazza, R Volpi, P Morerio, A Cavallo, C Becchio, V Murino
International Journal of Computer Vision (IJCV), 2019
On the Road to Online Adaptation for Semantic Image Segmentation
R Volpi, P de Jorge, D Larlus, G Csurka
Computer Vision and Pattern Recognition (CVPR), 2022
Semantic image segmentation: Two decades of research
G Csurka, R Volpi, B Chidlovskii
Foundations and Trends® in Computer Graphics and Vision 14 (1-2), 1-162, 2022
Modeling a population of retinal ganglion cells with restricted Boltzmann machines
R Volpi, M Zanotto, A Maccione, S Di Marco, L Berdondini, D Sona, ...
Scientific Reports 10 (16549), 2020
Reliability in Semantic Segmentation: Are We on the Right Track?
P de Jorge, R Volpi, P Torr, G Rogez
Computer Vision and Pattern Recognition (CVPR), 2023
RaSP: Relation-aware Semantic Prior for Weakly Supervised Incremental Segmentation
S Roy, R Volpi, G Csurka, D Larlus
Conference on Lifelong Learning Agents (CoLLAs), 2023
Understanding action concepts from videos and brain activity through subjects’ consensus
J Cavazza, W Ahmed, R Volpi, P Morerio, F Bossi, C Willemse, ...
Scientific Reports 12 (1), 19073, 2022
PANDAS: Prototype-based Novel Class Discovery and Detection
TL Hayes, CR de Souza, N Kim, J Kim, R Volpi, D Larlus
arXiv:2402.17420 [cs.CV], 2024
Placing Objects in Context via Inpainting for Out-of-distribution Segmentation
P de Jorge, R Volpi, PK Dokania, PHS Torr, G Rogez
arXiv:2402.16392 [cs.CV], 2024
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