High spatial resolution (HSR) remote sensing images contain complex foreground-
background relationships, which makes the remote sensing land cover segmentation a …

Feature selection attempts to capture the more discriminative features and plays a significant
role in multilabel learning. As an efficient mathematical tool to handle incomplete and …

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 …

Multi-label learning is a rapidly growing research area that aims to predict multiple labels
from a single input data point. In the era of big data, tasks involving multi-label classification …

The purpose of multi-label image classification task is to accurately assign a set of labels to
the objects in images. Although promising results have been achieved, most of the existing …

For cooling equipment management and scheduling optimization, accurate building cooling
load forecasting technology is crucial. Currently, the physics-based forecasting models are …

Multilabel image classification aims to assign images to multiple possible labels. In this task,
each image may be associated with multiple labels, making it more challenging than the …

Multimodal multi-label emotion recognition (MMER) is a vital yet challenging task in affective
computing. Despite significant progress in previous works, there are three limitations:(i) …

Ingredient prediction has received more and more attention with the help of image
processing for its diverse real-world applications, such as nutrition intake management and …

The goal of Few-shot classification (FSL) is to identify unseen classes with very limited
samples has attracted more and more attention. Usually, it is formulated as a metric learning …