65 citations · 124 across the 34 of their papers we have counts for
38 papers
Text-to-CAD Evaluation with CADTests
Dimitrios Mallis, Marco Wang, Ahmet Serdar Karadeniz +3
Text-to-CAD has recently emerged as an important task with the potential to substantially accelerate design workflows. Despite its significance, there has been surprisingly little…
LAA-X: Unified Localized Artifact Attention for Quality-Agnostic and Generalizable Face Forgery Detection
Dat Nguyen, Enjie Ghorbel, Anis Kacem +2
In this paper, we propose Localized Artifact Attention X (LAA-X), a novel deepfake detection framework that is both robust to high-quality forgeries and capable of generalizing to…
Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimation
Nassim Ali Ousalah, Peyman Rostami, Vincent Gaudillière +4
In this paper, we address the problem of 6-DoF object pose estimation from a single RGB image. Indirect methods that typically predict intermediate 2D keypoints, followed by a Pers…
Enabling Training-Free Text-Based Remote Sensing Segmentation
Jose Sosa, Danila Rukhovich, Anis Kacem +1
Recent advances in Vision Language Models (VLMs) and Vision Foundation Models (VFMs) have opened new opportunities for zero-shot text-guided segmentation of remote sensing imagery.…
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…
CAD-Recode: Reverse Engineering CAD Code from Point Clouds
Danila Rukhovich, Elona Dupont, Dimitrios Mallis +3
Computer-Aided Design (CAD) models are typically constructed by sequentially drawing parametric sketches and applying CAD operations to obtain a 3D model. The problem of 3D CAD rev…
CAD-Assistant: Tool-Augmented VLLMs as Generic CAD Task Solvers
Dimitrios Mallis, Ahmet Serdar Karadeniz, Sebastian Cavada +5
We propose CAD-Assistant, a general-purpose CAD agent for AI-assisted design. Our approach is based on a powerful Vision and Large Language Model (VLLM) as a planner and a tool-aug…
Hybrid Attention for Robust RGB-T Pedestrian Detection in Real-World Conditions
Arunkumar Rathinam, Leo Pauly, Abd El Rahman Shabayek +4
Multispectral pedestrian detection has gained significant attention in recent years, particularly in autonomous driving applications. To address the challenges posed by adversarial…
DAVINCI: A Single-Stage Architecture for Constrained CAD Sketch Inference
Ahmet Serdar Karadeniz, Dimitrios Mallis, Nesryne Mejri +3
This work presents DAVINCI, a unified architecture for single-stage Computer-Aided Design (CAD) sketch parameterization and constraint inference directly from raster sketch images.…
How Effective is Pre-training of Large Masked Autoencoders for Downstream Earth Observation Tasks?
Jose Sosa, Mohamed Aloulou, Danila Rukhovich +4
Self-supervised pre-training has proven highly effective for many computer vision tasks, particularly when labelled data are scarce. In the context of Earth Observation (EO), found…
TransCAD: A Hierarchical Transformer for CAD Sequence Inference from Point Clouds
Elona Dupont, Kseniya Cherenkova, Dimitrios Mallis +3
3D reverse engineering, in which a CAD model is inferred given a 3D scan of a physical object, is a research direction that offers many promising practical applications. This paper…
PICASSO: A Feed-Forward Framework for Parametric Inference of CAD Sketches via Rendering Self-Supervision
Ahmet Serdar Karadeniz, Dimitrios Mallis, Nesryne Mejri +3
This work introduces PICASSO, a framework for the parameterization of 2D CAD sketches from hand-drawn and precise sketch images. PICASSO converts a given CAD sketch image into para…
Multi-Objective Hardware Aware Neural Architecture Search using Hardware Cost Diversity
Nilotpal Sinha, Peyman Rostami, Abd El Rahman Shabayek +2
Hardware-aware Neural Architecture Search approaches (HW-NAS) automate the design of deep learning architectures, tailored specifically to a given target hardware platform. Yet, th…
CAD-SIGNet: CAD Language Inference from Point Clouds using Layer-wise Sketch Instance Guided Attention
Mohammad Sadil Khan, Elona Dupont, Sk Aziz Ali +3
Reverse engineering in the realm of Computer-Aided Design (CAD) has been a longstanding aspiration, though not yet entirely realized. Its primary aim is to uncover the CAD process…
LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection
Dat Nguyen, Nesryne Mejri, Inder Pal Singh +5
This paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Network (LAA-Net). Existing methods for high-quality deepfake detecti…
Hardware Aware Evolutionary Neural Architecture Search using Representation Similarity Metric
Nilotpal Sinha, Abd El Rahman Shabayek, Anis Kacem +3
Hardware-aware Neural Architecture Search (HW-NAS) is a technique used to automatically design the architecture of a neural network for a specific task and target hardware. However…
SHARP Challenge 2023: Solving CAD History and pArameters Recovery from Point clouds and 3D scans. Overview, Datasets, Metrics, and Baselines
Dimitrios Mallis, Sk Aziz Ali, Elona Dupont +6
Recent breakthroughs in geometric Deep Learning (DL) and the availability of large Computer-Aided Design (CAD) datasets have advanced the research on learning CAD modeling processe…
Impact of Disentanglement on Pruning Neural Networks
Carl Shneider, Peyman Rostami, Anis Kacem +3
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…