[WACV 2024 Oral] - ARNIQA: Learning Distortion Manifold for Image Quality Assessment
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Updated
Nov 25, 2024 - Python
[WACV 2024 Oral] - ARNIQA: Learning Distortion Manifold for Image Quality Assessment
Official implementation for CVPR2023 Paper "Re-IQA : Unsupervised Learning for Image Quality Assessment in the Wild"
[ECCV 2022] We investigated a broad range of neural network elements and developed a robust perceptual similarity metric. Our shift-tolerant perceptual similarity metric (ST-LPIPS) is consistent with human perception and is less susceptible to imperceptible misalignments between two images than existing metrics.
[TMLR 2023] as a featured article (spotlight 🌟 or top 0.01% of the accepted papers). In this study, we systematically examine the robustness of both traditional and learned perceptual similarity metrics to imperceptible adversarial perturbations.
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