3 papers
cs.CV2026
IPAD-CLIP: Teaching CLIP to Detect Image Local Perceptual Artifacts
Juan Wang, Xinyu Sun, Ke Zhang +4
Current image quality assessment methods are heavily biased towards global distortions (e.g., noise, blur), neglecting local perceptual artifacts such as ghosting, lens flare, and…
cs.CV2025
NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment
Shuhao Han, Haotian Fan, Fangyuan Kong +112
This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment, which will be held in conjunction with the New Trends in Image Restoratio…
cs.CV2024
SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning
Zewen Chen, Juan Wang, Wen Wang +8
Existing Image Quality Assessment (IQA) methods achieve remarkable success in analyzing quality for overall image, but few works explore quality analysis for Regions of Interest (R…