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cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

TALON: Token-Aligned Lightweight Adapters for 6-DoF Spacecraft Pose Estimation

Abid Ali, Arunkumar Rathinam, Djamila Aouada

Monocular 6-DoF spacecraft pose estimation methods predominantly process individual frames, discarding the temporal information present in an image sequence acquired during spacecr…

cs.CV2026

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…

cs.CV2026

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…

cs.CV2026

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…