The simultaneous recognition of multiple objects in one image remains a challenging task,
spanning multiple events in the recognition field such as various object scales, inconsistent …

Multi-label image recognition is a practical and challenging task compared to single-label
image classification. However, previous works may be suboptimal because of a great …

Multi-label image classification is a prediction task that aims to identify more than one label
from a given image. This paper considers the semantic consistency of the latent space …

Multi-label image classification refers to assigning a set of labels for an image. One of the
main challenges of this task is how to effectively capture the correlation among labels …

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 …

Label-specific features serve as an effective strategy to learn from multi-label data, where a
set of features encoding specific characteristics of each label are generated to help induce …

Multi-label image classification is a fundamental but challenging task in computer vision. To
tackle the problem, the label-related semantic information is often exploited, but the …

Multi-label image recognition aims to recognize multiple objects simultaneously in one
image. Recent ideas to solve this problem have focused on learning dependencies of label …

Multi-label image classification is a fundamental yet challenging task in computer vision that
aims to identify multiple objects from a given image. Recent studies on this task mainly focus …

In multi-label classification, the strategy of label-specific features has been shown to be
effective to learn from multi-label examples by accounting for the distinct discriminative …