With the rapid advancement of big data technology, high-dimensional datasets comprising
multi-label data have become prevalent in various fields. However, these datasets often …

Label distribution learning (LDL) is a recent hot topic, in which ambiguity is modeled via
description degrees of the labels. However, in common LDL tasks, eg, age estimation, labels …

A novel approach to rank estimation, called geometric order learning (GOL), is proposed in
this paper. First, we construct an embedding space, in which the direction and distance …

In this paper, our objective is to simultaneously explore the learning of ordinal relationships
among age labels and address the challenge of heterogeneous data resulting from the non …

Facial age estimation aims to rank the face aging data by taking in the correlation among
age categories. Conventional age estimation models are trained based on assumed high …

In contrast to multi-label learning, label distribution learning characterizes the polysemy of
examples by a label distribution to represent richer semantics. In the learning process of …

Comparing different age estimation methods poses a challenge due to the unreliability of
published results stemming from inconsistencies in the benchmarking process. Previous …

Abstract Ordinal classification (OC), ie, labeling instances along classes with a natural
ordering, is common in multiple applications such as size or budget based …

We present a novel language-driven ordering alignment method for ordinal classification.
The labels in ordinal classification contain additional ordering relations, making them prone …

Retinal age has recently emerged as a reliable ageing biomarker for assessing risks of
ageing-related diseases. Several studies propose to train deep learning models to estimate …