no code implementations • 27 Feb 2024 • Mohammad Sadil Khan, Elona Dupont, Sk Aziz Ali, Kseniya Cherenkova, Anis Kacem, Djamila Aouada
Thanks to its auto-regressive nature, CAD-SIGNet not only reconstructs a unique full design history of the corresponding CAD model given an input point cloud but also provides multiple plausible design choices.
no code implementations • 24 Jan 2024 • Dat Nguyen, Nesryne Mejri, Inder Pal Singh, Polina Kuleshova, Marcella Astrid, Anis Kacem, Enjie Ghorbel, Djamila Aouada
Second, an Enhanced Feature Pyramid Network (E-FPN) is proposed as a simple and effective mechanism for spreading discriminative low-level features into the final feature output, with the advantage of limiting redundancy.
no code implementations • 7 Nov 2023 • Nilotpal Sinha, Abd El Rahman Shabayek, Anis Kacem, Peyman Rostami, Carl Shneider, Djamila Aouada
Our approach re-frames the neural architecture search problem as finding an architecture with performance similar to that of a reference model for a target hardware, while adhering to a cost constraint for that hardware.
Hardware Aware Neural Architecture Search Neural Architecture Search
1 code implementation • 30 Aug 2023 • Dimitrios Mallis, Sk Aziz Ali, Elona Dupont, Kseniya Cherenkova, Ahmet Serdar Karadeniz, Mohammad Sadil Khan, Anis Kacem, Gleb Gusev, Djamila Aouada
In this paper, we define the proposed SHARP 2023 tracks, describe the provided datasets, and propose a set of baseline methods along with suitable evaluation metrics to assess the performance of the track solutions.
no code implementations • 19 Jul 2023 • Carl Shneider, Peyman Rostami, Anis Kacem, Nilotpal Sinha, Abd El Rahman 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.
1 code implementation • 22 May 2023 • Ashish Sinha, Jeremy Kawahara, Arezou Pakzad, Kumar Abhishek, Matthieu Ruthven, Enjie Ghorbel, Anis Kacem, Djamila Aouada, Ghassan Hamarneh
In recent years, deep learning (DL) has shown great potential in the field of dermatological image analysis.
no code implementations • 13 Apr 2023 • Kseniya Cherenkova, Elona Dupont, Anis Kacem, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada
3D scanning as a technique to digitize objects in reality and create their 3D models, is used in many fields and areas.
no code implementations • 25 Jan 2023 • Indel Pal Singh, Enjie Ghorbel, Anis Kacem, Arunkumar Rathinam, Djamila Aouada
In this paper, a discriminator-free adversarial-based Unsupervised Domain Adaptation (UDA) for Multi-Label Image Classification (MLIC) referred to as DDA-MLIC is proposed.
Multi-Label Image Classification Unsupervised Domain Adaptation
no code implementations • 22 Aug 2022 • Elona Dupont, Kseniya Cherenkova, Anis Kacem, Sk Aziz Ali, Ilya Arzhannikov, Gleb Gusev, Djamila Aouada
3D reverse engineering is a long sought-after, yet not completely achieved goal in the Computer-Aided Design (CAD) industry.
1 code implementation • 18 Aug 2022 • Ahmet Serdar Karadeniz, Sk Aziz Ali, Anis Kacem, Elona Dupont, Djamila Aouada
We propose a new neural network architecture for 3D body shape and high-resolution texture completion -- BCom-Net -- that can reconstruct the full geometry from mid-level to high-level partial input scans.
no code implementations • 20 Apr 2021 • Anis Kacem, Kseniya Cherenkova, Djamila Aouada
The proposed network consists of three components; (1) a Graph Convolutional Autoencoder (GCA) to encode the 3D faces into latent representations, (2) a Generative Adversarial Network (GAN) that translates the latent representations of expressive faces into those of neutral faces, (3) and an identity recognition sub-network taking advantage of the neutralized latent representations for 3D face recognition.
no code implementations • 19 Apr 2021 • Konstantinos Papadopoulos, Anis Kacem, Abdelrahman Shabayek, Djamila Aouada
This has two disadvantages.
no code implementations • 26 Oct 2020 • Alexandre Saint, Anis Kacem, Kseniya Cherenkova, Konstantinos Papadopoulos, Julian Chibane, Gerard Pons-Moll, Gleb Gusev, David Fofi, Djamila Aouada, Bjorn Ottersten
Additionally, two unique datasets of 3D scans are proposed, to provide raw ground-truth data for the benchmarks.
no code implementations • 23 Oct 2020 • Alexandre Saint, Anis Kacem, Kseniya Cherenkova, Djamila Aouada
The texture is subsequently obtained by projecting the partial texture onto the template mesh before inpainting the corresponding texture map with a novel approach.
no code implementations • 23 Jul 2019 • Naima Otberdout, Mohamed Daoudi, Anis Kacem, Lahoucine Ballihi, Stefano Berretti
In this work, we propose a novel approach for generating videos of the six basic facial expressions given a neutral face image.
no code implementations • 25 Oct 2018 • Naima Otberdout, Anis Kacem, Mohamed Daoudi, Lahoucine Ballihi, Stefano Berretti
In this paper, we propose a new approach for facial expression recognition using deep covariance descriptors.
no code implementations • 29 Jun 2018 • Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Stefano Berretti, Juan Carlos Alvarez-Paiva
We derived then geometric and computational tools for rate-invariant analysis and adaptive re-sampling of trajectories, grounding on the Riemannian geometry of the underlying manifold.
no code implementations • 10 May 2018 • Naima Otberdout, Anis Kacem, Mohamed Daoudi, Lahoucine Ballihi, Stefano Berretti
In this paper, covariance matrices are exploited to encode the deep convolutional neural networks (DCNN) features for facial expression recognition.
no code implementations • ICCV 2017 • Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Juan Carlos Alvarez-Paiva
In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation.
Facial Expression Recognition Facial Expression Recognition (FER)
no code implementations • ICCV 2017 • Anis Kacem, Mohamed Daoudi, Boulbaba Ben Amor, Juan Carlos Alvarez-Paiva
In this paper, we study the problem of facial expression recognition using a novel space-time geometric representation.
Facial Expression Recognition Facial Expression Recognition (FER)