25 citations · 75 across the 26 of their papers we have counts for
9 papers · 1 filter
An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection
Romain Hermary, Nesryne Mejri, Djamila Aouada
Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging. Although metrics such as AUROC, AUPR, F1-score…
Unsupervised Domain Adaptation for Sim-to-Real Object Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement
Nidhal Eddine Chenni, Arunkumar Rathinam, Djamila Aouada
Unsupervised domain adaptation (UDA) enables robust transfer of knowledge from simulated to real environments while exploiting a subset of unlabeled target data to improve real-wor…
When AUC Misleads: Polarization-Aware Evaluation of Deepfake Detectors under Domain Shift
Dat Nguyen, Cosmin Radoi, Romain Hermary +4
Recent advances in generative AI, such as diffusion models and face-swapping tools, have enabled the creation of highly realistic deepfakes, leading to real-world harms including f…
Efficient Onboard Spacecraft Pose Estimation with Event Cameras and Neuromorphic Hardware
Arunkumar Rathinam, Jules Lecomte, Jost Reelsen +3
Reliable relative pose estimation is a key enabler for autonomous rendezvous and proximity operations, yet space imagery is notoriously challenging due to extreme illumination, hig…
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…