Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Enrico Fini"'
Autor:
Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Moin Nabi, Xavier Alameda-Pineda, Elisa Ricci
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, pp.1-14. ⟨10.1109/TPAMI.2022.3163806⟩
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, pp.1-14. ⟨10.1109/TPAMI.2022.3163806⟩
A fundamental and challenging problem in deep learning is catastrophic forgetting, i.e. the tendency of neural networks to fail to preserve the knowledge acquired from old tasks when learning new tasks. This problem has been widely investigated in th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6f92f0c45bc886a88a83e45ba4e50f85
https://inria.hal.science/hal-03908664
https://inria.hal.science/hal-03908664
Publikováno v:
Università degli di Trento-IRIS
This paper presents solo-learn, a library of self-supervised methods for visual representation learning. Implemented in Python, using Pytorch and Pytorch lightning, the library fits both research and industry needs by featuring distributed training p
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::28426a7ce7816a37e7f58a6f3f2f8fc9
https://doi.org/10.5281/zenodo.6363321
https://doi.org/10.5281/zenodo.6363321
Autor:
Guanglei Yang, Enrico Fini, Dan Xu, Paolo Rota, Mingli Ding, Tang Hao, Xavier Alameda-Pineda, Elisa Ricci
Publikováno v:
IEEE Transactions on Multimedia
IEEE Transactions on Multimedia, 2022, ⟨10.1109/TMM.2022.3167555⟩
IEEE Transactions on Multimedia, 2022, ⟨10.1109/TMM.2022.3167555⟩
International audience; Over the past years, semantic segmentation, as many other tasks in computer vision, benefited from the progress in deep neural networks, resulting in significantly improved performance. However, deep architectures trained with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::82d41d3a7dcc17ece6bdf9765fb1486c
https://ieeexplore.ieee.org/document/9757872/authors#authors
https://ieeexplore.ieee.org/document/9757872/authors#authors
Autor:
Enrico Fini, Victor G. Turrisi Da Costa, Xavier Alameda-Pineda, Elisa Ricci, Karteek Alahari, Julien Mairal
Publikováno v:
CVPR 2022-IEEE/CVF Conference on Computer Vision and Pattern Recognition
CVPR 2022-IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2022, New Orleans, United States. pp.9611-9620, ⟨10.1109/CVPR52688.2022.00940⟩
CVPR 2022-IEEE/CVF Conference on Computer Vision and Pattern Recognition, Jun 2022, New Orleans, United States. pp.9611-9620, ⟨10.1109/CVPR52688.2022.00940⟩
International audience; Self-supervised models have been shown to produce comparable or better visual representations than their supervised counterparts when trained offline on unlabeled data at scale. However, their efficacy is catastrophically redu
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8262add4fab99b997aace86b7c0252f
http://arxiv.org/abs/2112.04215
http://arxiv.org/abs/2112.04215
Publikováno v:
CVPR
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
In this paper, we address Novel Class Discovery (NCD), the task of unveiling new classes in a set of unlabeled samples given a labeled dataset with known classes. We exploit the peculiarities of NCD to build a new framework, named Neighborhood Contra
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e1206182d4bd41f5b20957cc1e83948
https://ieeexplore.ieee.org/document/9578829
https://ieeexplore.ieee.org/document/9578829
Publikováno v:
European Conference on Computer Vision
European Conference on Computer Vision, Aug 2020, edinburgh, United Kingdom
Computer Vision – ECCV 2020 ISBN: 9783030586034
ECCV (28)
European Conference on Computer Vision, Aug 2020, edinburgh, United Kingdom
Computer Vision – ECCV 2020 ISBN: 9783030586034
ECCV (28)
Continual Learning (CL) aims to develop agents emulating the human ability to sequentially learn new tasks while being able to retain knowledge obtained from past experiences. In this paper, we introduce the novel problem of Memory-Constrained Online
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f010fde3f88fe5f8c08f20d36c707ca6
https://hal.telecom-paris.fr/hal-02941923
https://hal.telecom-paris.fr/hal-02941923
Autor:
Elisa Ricci, Federico Mento, Ruud J. G. van Sloun, Sebastiaan P. Oei, Iris A.M. Huijben, Giovanni Maschietto, Libertario Demi, Nishith Chennakeshava, Riccardo Inchingolo, Andrea Smargiassi, Andrea Passerini, Gino Soldati, Alessandro Sentelli, Enrico Fini, Ben Luijten, Willi Menapace, Subhankar Roy, Paolo Rota, Elena Torri, Cristiano Saltori, Riccardo Trevisan, Emanuele Peschiera
Publikováno v:
IEEE Transactions on Medical Imaging
IEEE Transactions on Medical Imaging, 39(8):9093068, 2676-2687. Institute of Electrical and Electronics Engineers
IEEE Transactions on Medical Imaging, 39(8):9093068, 2676-2687. Institute of Electrical and Electronics Engineers
Deep learning (DL) has proved successful in medical imaging and, in the wake of the recent COVID-19 pandemic, some works have started to investigate DL-based solutions for the assisted diagnosis of lung diseases. While existing works focus on CT scan
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d9f8a432b0ec644dd6103c47069e2246
https://pubmed.ncbi.nlm.nih.gov/32406829
https://pubmed.ncbi.nlm.nih.gov/32406829
Autor:
Enrico Fini, Alessio Brutti
Publikováno v:
ICASSP
Recently, a fully supervised speaker diarization approach was proposed (UIS-RNN) which models speakers using multiple instances of a parameter-sharing recurrent neural network. In this paper we propose qualitative modifications to the model that sign
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18e4419c634976eeb7510a9a9a245cf5
https://doi.org/10.1109/icassp40776.2020.9053477
https://doi.org/10.1109/icassp40776.2020.9053477
Autor:
Riccardo Franceschini, Enrico Fini, Cigdem Beyan, Alessandro Conti, Federica Arrigoni, Elisa Ricci
Publikováno v:
2022 26th International Conference on Pattern Recognition (ICPR)
Emotion recognition is involved in several real-world applications. With an increase in available modalities, automatic understanding of emotions is being performed more accurately. The success in Multimodal Emotion Recognition (MER), primarily relie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7287ab1901f35116308c8a86f5793545