3 papers
cs.CV2026
From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift
Firas Gabetni, Alexandre Rocchi Henry, Alexandre Rocchi +3
Detecting covariate shift is critical for building reliable vision systems. While most prior work focuses on improving robustness to shift, explicitly detecting covariate shift rem…
cs.LG2025
Foundation Models and Transformers for Anomaly Detection: A Survey
Mouïn Ben Ammar, Arturo Mendoza, Nacim Belkhir +2
In line with the development of deep learning, this survey examines the transformative role of Transformers and foundation models in advancing visual anomaly detection (VAD). We ex…
cs.AI2024
Towards Understanding and Quantifying Uncertainty for Text-to-Image Generation
Gianni Franchi, Dat Nguyen Trong, Nacim Belkhir +2
Uncertainty quantification in text-to-image (T2I) generative models is crucial for understanding model behavior and improving output reliability. In this paper, we are the first to…