Historical Color Image Dating
4 papers with code • 1 benchmarks • 1 datasets
Most implemented papers
Non-parametric Uni-modality Constraints for Deep Ordinal Classification
We propose a new constrained-optimization formulation for deep ordinal classification, in which uni-modality of the label distribution is enforced implicitly via a set of inequality constraints over all the pairs of adjacent labels.
Deep Ordinal Regression with Label Diversity
By discretizing the target into a set of non-overlapping classes, it has been shown that training a classifier can improve neural network accuracy compared to using a standard regression approach.
Learning Probabilistic Ordinal Embeddings for Uncertainty-Aware Regression
An ordinal distribution constraint is proposed to exploit the ordinal nature of regression.
OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression
In this paper, we propose to learn the rank concepts from the rich semantic CLIP latent space.