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20232026
most citedFoundation Models and Transformers for Anomaly Detection: A Survey

15 citations · 21 across the 7 of their papers we have counts for

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6 papers · 1 filter

cs.CV2026

Disentangling Hallucinations: Orthogonal Semantic Projection for Robust Interpretability

Emirhan Bilgiç, Baptiste Caramiaux, Zhi Yan +1

As Vision-Language Models are increasingly deployed in safety-critical applications, the trustworthiness of their explanations becomes crucial. Explainable AI (XAI) methods for Vis…

cs.CV2026

SS3D: End2End Self-Supervised 3D from Web Videos

Marwane Hariat, Gianni Franchi, David Filliat +1

We present SS3D, a web-scale SfM-based self-supervision pretraining pipeline for feed-forward 3D estimation from monocular video. Our model jointly predicts depth, ego-motion, and…

cs.CV2026

From Local Geometry to Global Pseudo Labeling for Robust Positive Unlabeled Learning under Covariate Shift

Firas Gabetni, Alexandre Rocchi, Nacim Belkhir +2

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.CV2025★ 1 cited

FakeParts: a New Family of AI-Generated DeepFakes

Ziyi Liu, Firas Gabetni, Awais Hussain Sani +5

We introduce FakeParts, a new class of deepfakes characterized by subtle, localized manipulations to specific spatial regions or temporal segments of otherwise authentic videos. Un…

cs.CV2025

COOkeD: Ensemble-based OOD detection in the era of zero-shot CLIP

Galadrielle Humblot-Renaux, Gianni Franchi, Sergio Escalera +1

Out-of-distribution (OOD) detection is an important building block in trustworthy image recognition systems as unknown classes may arise at test-time. OOD detection methods typical…

cs.CV2023★ 2 cited

Scaling for Training Time and Post-hoc Out-of-distribution Detection Enhancement

Kai Xu, Rongyu Chen, Gianni Franchi +1

The capacity of a modern deep learning system to determine if a sample falls within its realm of knowledge is fundamental and important. In this paper, we offer insights and analys…