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DiffoR proposes a novel continuous generative framework for ordinal regression using diffusion models, overcoming limitations of discrete methods. Extensive experiments on 12 benchmarks demonstrate state-of-the-art performance across four domains.
This paper proposes a generative framework for emotion intensity evaluation, shifting from discrete classification to continuous 0-100 scoring. It demonstrates superior performance and generalization in domains like finance.
ChangeFlow presents a generative framework for remote sensing change detection that synthesizes change masks in latent space using rectified flow, achieving improved accuracy and robustness through sampling-based prediction ensembling, with an average F1 of 80.4% across four benchmarks.