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Investigating pretrained self-supervised vision transformers for reference-based quality assessment
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Embedded Internet Systems Lab.ORCID iD: 0000-0003-0221-8268
2023 (English)Conference paper, Published paper (Refereed)
Place, publisher, year, edition, pages
Society for Imaging Science and Technology , 2023. Vol. 35, article id IQSP-308
Series
IS&T International Symposium on Electronic Imaging Science and Technology, E-ISSN 2470-1173
Keywords [en]
Full-reference Image Quality Assessment, Vision Transformers, Self-Supervised Learning (DINO)
National Category
Computer graphics and computer vision Computer Sciences
Research subject
Machine Learning
Identifiers
URN: urn:nbn:se:ltu:diva-103562DOI: 10.2352/EI.2023.35.8.IQSP-308Scopus ID: 2-s2.0-85169569117OAI: oai:DiVA.org:ltu-103562DiVA, id: diva2:1826836
Conference
IS and T International Symposium on Electronic Imaging: 20th Image Quality and System Performance, IQSP 2023, San Francisco, United States, January 16-19, 2023
Available from: 2024-01-12 Created: 2024-01-12 Last updated: 2025-02-01Bibliographically approved

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De, Kanjar

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