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Enjie Ghorbel

8 accepted papers

2026

Cov2Pose: Leveraging Spatial Covariance for Direct Manifold-aware 6-DoF Object Pose Estimation

Nassim Ali Ousalah, Peyman Rostami, Vincent Gaudillière, Emmanuel Koumandakis, Anis Kacem, Enjie Ghorbel +1
CVPR 2026

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 Perspective-n-Point solver, have shown great performance. Direct approaches, which regress the pose in an end-to-end manner,

Cited by 0Source ↗Scholar ↗
2025

Uncertainty-Aware Knowledge Distillation for Compact and Efficient 6DoF Pose Estimation

Nassim Ali Ousalah, Anis Kacem, Enjie Ghorbel, Emmanuel Koumandakis, Djamila Aouada
IROS 2025

Compact and efficient 6DoF object pose estimation is crucial in applications such as robotics, augmented reality, and space autonomous navigation systems, where lightweight models are critical for real-time accurate performance. This paper introduces a novel uncertainty-aware end-to-end Knowledge Di

Cited by 2Source ↗Scholar ↗
2025

Vulnerability-Aware Spatio-Temporal Learning for Generalizable Deepfake Video Detection

Dat Nguyen, Marcella Astrid, Anis Kacem, Enjie Ghorbel, Djamila Aouada
ICCV 2025poster

Detecting deepfake videos is highly challenging given the complexity of characterizing spatio-temporal artifacts. Most existing methods rely on binary classifiers trained using real and fake image sequences, therefore hindering their generalization capabilities to unseen generation methods. Moreover…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗
2024

A Hitchhiker's Guide to Fine-Grained Face Forgery Detection Using Common Sense Reasoning

Niki Foteinopoulou, Enjie Ghorbel, Djamila Aouada
NeurIPS 2024poster

Explainability in artificial intelligence is crucial for restoring trust, particularly in areas like face forgery detection, where viewers often struggle to distinguish between real and fabricated content. Vision and Large Language Models (VLLM) bridge computer vision and natural language, offering…

Cited by 3Source ↗PDF ↗code ↗Scholar ↗
2024

LAA-Net: Localized Artifact Attention Network for Quality-Agnostic and Generalizable Deepfake Detection

Dat Nguyen, Nesryne Mejri, Inder Pal Singh, Polina Kuleshova, Marcella Astrid, Anis Kacem +2
CVPR 2024poster

This paper introduces a novel approach for high-quality deepfake detection called Localized Artifact Attention Network (LAA-Net). Existing methods for high-quality deepfake detection are mainly based on a supervised binary classifier coupled with an implicit attention mechanism. As a result they do…

Cited by 29Source ↗PDF ↗code ↗Scholar ↗
2023

UNTAG: Learning Generic Features for Unsupervised Type-Agnostic Deepfake Detection

Nesryne Mejri, Enjie Ghorbel, Djamila Aouada
ICASSP 2023accepted

This paper introduces a novel framework for unsupervised type-agnostic deepfake detection called UNTAG. Existing methods are generally trained in a supervised manner at the classification level, focusing on detecting at most two types of forgeries; thus, limiting their generalization capability acro…

Cited by 0Source ↗Scholar ↗
2019

View-invariant Action Recognition from RGB Data via 3D Pose Estimation

Renato Baptista, Enjie Ghorbel, Konstantinos Papadopoulos, Girum G. Demisse, Djamila Aouada, Björn E. Ottersten
ICASSP 2019accepted

In this paper, we propose a novel view-invariant action recognition method using a single monocular RGB camera. View-invariance remains a very challenging topic in 2D action recognition due to the lack of 3D information in RGB images. Most successful approaches make use of the concept of knowledge t…

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