Work

Comparative Study of DINOv2, I-JEPA, and ViT Embeddings for Unsupervised Anomaly Detection

Year: 2025

Type: conference-paper

Source: International Conference on Image and Vision Computing New Zealand (IVCNZ)

DOI: 10.1109/ivcnz67716.2025.11281645

Authors: Jean-Marc Spat, Houda Chabbi-Drissi

Institutions: School of Engineering and Architecture of Fribourg, HES-SO Fribourg

Language: English

Topic: Anomaly Detection Techniques and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: closed

OpenAlex ID: W4417249486

FWCI: 1.009

Cites: 12

Cited by: 1

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