A soft-weighted contrastive loss using rank-gap affinity and disparity terms learns globally consistent ordinal embeddings and reaches SOTA on age, BIQA, and BVQA benchmarks.
(a) Successful cases (b) Failure case 22℃ [34℃]1℃ [1℃] 11℃ [11℃] 17℃ [17℃] 21℃ [21℃] 30℃ [30℃] Figure 8.Examples of regression results on the SkyFinder dataset
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Contrastive Order Learning: A General Framework for Ordinal Regression
A soft-weighted contrastive loss using rank-gap affinity and disparity terms learns globally consistent ordinal embeddings and reaches SOTA on age, BIQA, and BVQA benchmarks.