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 recognition (MLR) with incomplete labels is very challenging. Recent works strive
to explore the image-to-label correspondence in the vision-language model, ie, CLIP, to …

Due to the expensive costs of collecting labels in multi-label classification datasets, partially
annotated multi-label classification has become an emerging field in computer vision. One …

Federated learning has been proposed as a privacy-preserving machine learning
framework that enables multiple clients to collaborate without sharing raw data. However …

Conventional multi-label classification (MLC) methods assume that all samples are fully
labeled and identically distributed. Unfortunately, this assumption is unrealistic in large …

A natural image frequently contains multiple classification targets, accordingly providing
multiple class labels rather than a single label per image. While the single-label …

Automatic security inspection relying on computer vision technology is a challenging task in
real-world scenarios due to many factors, such as intra-class variance, class imbalance, and …

Pseudo-labeling has emerged as a popular and effective approach for utilizing unlabeled
data. However, in the context of semi-supervised multi-label learning (SSMLL), conventional …

Partially annotated images are easy to obtain in multi-label classification. However,
unknown labels in partially annotated images exacerbate the positive-negative imbalance …

Multilabel representation learning is recognized as a challenging problem that can be
associated with either label dependencies between object categories or data-related issues …