In multi-dimensional classification (MDC), the semantics of objects are characterized by
multiple class spaces from different dimensions. Most MDC approaches try to explicitly …

Multi-dimensional classification (MDC) aims at learning from objects where each of them is
represented by a single instance while associated with multiple class variables. In recent …

In multi-dimensional classification (MDC), there are multiple class variables in the output
space with each of them corresponding to one heterogeneous class space. Due to the …

Partial-label learning (PLL) solves the problem where each training instance is assigned a
candidate label set, among which only one is the ground-truth label. The core of PLL is to …

In multi-dimensional classification (MDC), a number of class variables are assumed in the
output space with each of them specifying the class membership wrt one heterogeneous …

Multi-dimensional classification (MDC) aims to build classification models for multiple
heterogenous class spaces simultaneously, where each class space characterizes the …

Real-world objects exhibit intricate semantic properties that can be characterized from a
multitude of perspectives, which necessitates the development of a model capable of …

In traditional classification framework, the semantics of each object is usually characterized
by annotating a single class label from one homogeneous label space. Nonetheless, objects …

With current success of large-scale pre-trained models (PTMs), how efficiently adapting
PTMs to downstream tasks has attracted tremendous attention, especially for PTMs with …

Exploiting correlations among class variables and using them to facilitate the learning
process are a key challenge of Multi-Dimensional Classification (MDC) problems. Label …