Zobrazeno 1 - 10
of 62
pro vyhledávání: '"Anis Kacem"'
Autor:
Carl Shneider, Peyman Rostami, Anis Kacem, Nilotpal Sinha, Abdelrahman Shabayek, Djamila Aouada
Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency. This can be realized via efficient model compression. D
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8415303a9df70c07fea6ee62afb6226c
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, In press, pp.1-1. ⟨10.1109/TPAMI.2020.3002500⟩
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44 (2), pp.848-863. ⟨10.1109/TPAMI.2020.3002500⟩
IEEE Transactions on Pattern Analysis and Machine Intelligence, Institute of Electrical and Electronics Engineers, In press, pp.1-1. ⟨10.1109/TPAMI.2020.3002500⟩
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44 (2), pp.848-863. ⟨10.1109/TPAMI.2020.3002500⟩
International audience; In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image. We propose to exploit the face geometry by modeling the facial landmarks motion as curves encoded
Publikováno v:
2022 8th International Conference on Virtual Reality (ICVR).
Publikováno v:
Pattern Recognition. ICPR International Workshops and Challenges ISBN: 9783030687625
ICPR Workshops (1)
ICPR Workshops (1)
In this paper, we propose a new approach for 3D dynamic face verification exploiting 3D facial deformations. First, 3D faces are encoded into low-dimensional representations describing the local deformations of the faces with respect to a mean face.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::822421db528672ab721a31d72cdfc265
https://doi.org/10.1007/978-3-030-68763-2_7
https://doi.org/10.1007/978-3-030-68763-2_7
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2020, 31 (10), pp.3892-3905. ⟨10.1109/TNNLS.2019.2947244⟩
IEEE Transactions on Neural Networks and Learning Systems, 2020, 31 (10), pp.3892-3905. ⟨10.1109/TNNLS.2019.2947244⟩
IEEE Transactions on Neural Networks and Learning Systems, IEEE, 2020, 31 (10), pp.3892-3905. ⟨10.1109/TNNLS.2019.2947244⟩
IEEE Transactions on Neural Networks and Learning Systems, 2020, 31 (10), pp.3892-3905. ⟨10.1109/TNNLS.2019.2947244⟩
In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors. The solution is based on the idea of encoding local and global Deep Convolutional Neural Network (DCNN) features extracted from still images
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d8b22c646d7c1ec6bf66d2738e19280e
https://hal.archives-ouvertes.fr/hal-02369410
https://hal.archives-ouvertes.fr/hal-02369410
Autor:
Gleb Gusev, Alexandre Saint, Kseniya Cherenkova, Julian Chibane, Djamila Aouada, Gerard Pons-Moll, Anis Kacem, Konstantinos Papadopoulos, Bjorn Ottersten, David Fofi
Publikováno v:
European Conference on Computer Vision
European Conference on Computer Vision, Aug 2020, Online conference, France
Computer Vision – ECCV 2020 Workshops ISBN: 9783030660956
ECCV Workshops (2)
BASE-Bielefeld Academic Search Engine
European Conference on Computer Vision, Aug 2020, Online conference, France
Computer Vision – ECCV 2020 Workshops ISBN: 9783030660956
ECCV Workshops (2)
BASE-Bielefeld Academic Search Engine
The SHApe Recovery from Partial textured 3D scans challenge, SHARP 2020, is the first edition of a challenge fostering and benchmarking methods for recovering complete textured 3D scans from raw incomplete data. SHARP 2020 is organised as a workshop
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ff00e5c3e49f006affb4eafdbf202653
https://hal.science/hal-03130337
https://hal.science/hal-03130337
Publikováno v:
Machine Learning for the Diagnosis and Treatment of Affective Disorders Workshop, 8th International Conference on Affective Computing & Intelligent Interaction (ACII 2019)
Machine Learning for the Diagnosis and Treatment of Affective Disorders Workshop, 8th International Conference on Affective Computing & Intelligent Interaction (ACII 2019), Sep 2019, Cambridge, United Kingdom
ACII Workshops
Machine Learning for the Diagnosis and Treatment of Affective Disorders Workshop, 8th International Conference on Affective Computing & Intelligent Interaction (ACII 2019), Sep 2019, Cambridge, United Kingdom
ACII Workshops
We propose an automatic method to measure depression severity from body movement dynamics in participants undergoing treatment for depression. Participants in a clinical trial for treatment of depression were interviewed on up to four occasions at 7-
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a726eb74d9e540b370ecddc0d8fd606e
https://hal.archives-ouvertes.fr/hal-02163140
https://hal.archives-ouvertes.fr/hal-02163140
Autor:
Ludwig Gebert, Anis Kacem, Pierre Guerreschi, Benjamin Szczapa, Mohamed Daoudi, Juan Carlos Alvarez-Paiva
Publikováno v:
2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019)
2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), May 2019, Lille, France. pp.1-5, ⟨10.1109/FG.2019.8756617⟩
The 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019)
The 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019), May 2019, Lille, France
FG
2019 14th IEEE International Conference on Automatic Face & Gesture Recognition (FG 2019), May 2019, Lille, France. pp.1-5, ⟨10.1109/FG.2019.8756617⟩
The 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019)
The 14th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2019), May 2019, Lille, France
FG
International audience; In this paper, we propose a novel technique for quantifying the facial asymmetry from 2D videos to evaluate facial paralysis treatments based on Botulinum Toxin (BT) injections. Our approach uses 2D facial landmarks and baryce
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6669ead5e27519a321b0a002da20f1a6
https://hal.science/hal-02374294
https://hal.science/hal-02374294
Publikováno v:
COMNET
In this paper, we present an applying of a finite volume variational approach to the mesh smoothing using local weights. This approach is able to effectively remove undesirable noise and provides a very good performance. While conserving outstanding