ML-CLIPSim aggregates multi-layer patch and global similarities from frozen CLIP to approximate machine utility for images and outperforms standard IQA metrics on machine-preference tasks while staying competitive on human data.
Image quality assessment: Unifying structure and texture similarity.IEEE transactions on pattern analysis and machine intelligence, 44(5):2567–2581
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
eess.IV 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
ML-CLIPSim: Multi-Layer CLIP Similarity for Machine-Oriented Image Quality
ML-CLIPSim aggregates multi-layer patch and global similarities from frozen CLIP to approximate machine utility for images and outperforms standard IQA metrics on machine-preference tasks while staying competitive on human data.