65 citations · 124 across the 34 of their papers we have counts for
7 papers · 1 filter
MiCADangelo: Fine-Grained Reconstruction of Constrained CAD Models from 3D Scans
Ahmet Serdar Karadeniz, Dimitrios Mallis, Danila Rukhovich +3
Computer-Aided Design (CAD) plays a foundational role in modern manufacturing and product development, often requiring designers to modify or build upon existing models. Converting…
FPG-NAS: FLOPs-Aware Gated Differentiable Neural Architecture Search for Efficient 6DoF Pose Estimation
Nassim Ali Ousalah, Peyman Rostami, Anis Kacem +3
We introduce FPG-NAS, a FLOPs-aware Gated Differentiable Neural Architecture Search framework for efficient 6DoF object pose estimation. Estimating 3D rotation and translation from…
MultiMAE Meets Earth Observation: Pre-training Multi-modal Multi-task Masked Autoencoders for Earth Observation Tasks
Jose Sosa, Danila Rukhovich, Anis Kacem +1
Multi-modal data in Earth Observation (EO) presents a huge opportunity for improving transfer learning capabilities when pre-training deep learning models. Unlike prior work that o…
Domain Adaptation for Multi-label Image Classification: a Discriminator-free Approach
Inder Pal Singh, Enjie Ghorbel, Anis Kacem +1
This paper introduces a discriminator-free adversarial-based approach termed DDA-MLIC for Unsupervised Domain Adaptation (UDA) in the context of Multi-Label Image Classification (M…
Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation
Nassim Ali Ousalah, Anis Kacem, Enjie Ghorbel +2
Compact and efficient 6DoF object pose estimation is crucial in applications such as robotics, augmented reality, and space autonomous navigation systems, where lightweight models…
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity
Nesryne Mejri, Enjie Ghorbel, Anis Kacem +3
This paper introduces the first fully unsupervised domain adaptation (UDA) framework for unsupervised anomaly detection (UAD). The performance of UAD techniques degrades significan…
Vulnerability-Aware Spatio-Temporal Learning for Generalizable Deepfake Video Detection
Dat Nguyen, Marcella Astrid, Anis Kacem +2
Detecting deepfake videos is highly challenging given the complexity of characterizing spatio-temporal artifacts. Most existing methods rely on binary classifiers trained using rea…