Mihai-Marian Puscas
Mihai-Marian Puscas
Video Researcher
Bestätigte E-Mail-Adresse bei unitn.it
Titel
Zitiert von
Zitiert von
Jahr
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
962018
Learning to remember: A synaptic plasticity driven framework for continual learning
O Ostapenko, M Puscas, T Klein, P Jahnichen, M Nabi
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019
852019
Unsupervised tube extraction using transductive learning and dense trajectories
MM Puscas, E Sangineto, D Culibrk, N Sebe
Proceedings of the IEEE international conference on computer vision, 1653-1661, 2015
322015
Joint graph learning and video segmentation via multiple cues and topology calibration
J Song, L Gao, MM Puscas, F Nie, F Shen, N Sebe
Proceedings of the 24th ACM international conference on Multimedia, 831-840, 2016
222016
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
112019
Low-shot learning from imaginary 3d model
F Pahde, M Puscas, J Wolff, T Klein, N Sebe, M Nabi
2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 978-985, 2019
82019
Multimodal Prototypical Networks for Few-shot Learning
F Pahde, M Puscas, T Klein, M Nabi
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021
72021
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
72019
Low-shot learning from imaginary 3D model
F Pahde, M Puscas, M Nabi, T Klein
US Patent 11,080,560, 2021
2021
Self-paced adversarial training for multimodal and 3D model few-shot learning
F Pahde, O Ostapenko, T Klein, M Nabi, M Puscas
US Patent 10,990,848, 2021
2021
Generative adversarial network with dynamic capacity expansion for continual learning
M Puscas, M Nabi, T Klein, O Ostapenko
US Patent App. 16/711,134, 2021
2021
Learning in Low Data Regimes for Image and Video Understanding
M Puscas
University of Trento, 2019
2019
Learning to remember: Dynamic Generative Memory for Continual Learning
O Ostapenko, M Puscas, T Klein, M Nabi
2018
Structured Coupled Generative Adversarial Networks for Unsupervised Monocular Depth Estimation Download PDF
MM Puscas, D Xu, A Pilzer, N Sebe
3DV 2019
Y Dai, Z Zhu, M Ferrera, A Boulch, J Moras, MM Puscas, D Xu, N Sebe, ...
Learning to Remember what to Remember: A Synaptic Plasticity Driven Framework
O Ostapenko, M Puscas, T Klein, P Jähnichen, M Nabi
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