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.
Table 9 shows that ConOrd achieves the best performance, outperforming all prior methods on both datasets
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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.