The model that corresponds to Q Align (ICML2024). Quick Start with AutoModel For this image, start an AutoModel scorer with transformers==4.36.1 : Result should be 1.911 (in range [1,5], higher is better). From paper: arxiv.org/abs/2312.17090 . Syllabus IQA Results (Spearman/Pearson/Kendall) Datasets KonIQ (NR IQA, seen) SPAQ (NR IQA, Seen) KADID (FR IQA, Seen) LIVE C (NR IQA, Unseen ) LIVE (FR IQA, Unseen ) CSIQ (FR IQA, Unseen ) AGIQA (AIGC, Unseen ) Previous SOTA 0.916/0.928 (MUSIQ, ICCV2021) 0.922/0.919 (LIQE, CVPR2023) 0.934/0.937 (CONTRIQUE, TIP2022) NA NA NA NA Q Align (IQA) 0.937/0.945/0.785 0.931/0.933/0.763 0.934/0.934/0.777 0.887/0.896/0.706 0.874/0.840/0.682 0.845/0.876/0.654 0.731/0.791/0.529 Q Align (IQA+VQA) 0.944 /0.949/ 0.797 0.931/0.934/0.764 0.952 / 0.953 / 0.809 0.892 / 0.899 / 0.715 0.874/0.846/0.684 0.852/0.876/0.663 0.739/0.782/0.526 OneAlign (IQA+IAA+VQA) 0.941/ 0.950 /0.791 0.932 / 0.935 / 0.766 0.941/0.942/0.791 0.881/0.894/0.699 0.887 / 0.856 / 0.699 0.881 / 0.906 / 0.699 0.801 / 0.838 / 0.602 IAA Results (Spearman/Pearson) Dataset AVA test VILA (CVPR, 2023) 0.774/0.774 LIQE (CVPR, 2023) 0.776/0.763 Aesthetic Predictor (retrained on AVA train) 0.721/0.723…
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