Abstract In Multi-View Multi-Label Learning, each instance is described by several
heterogeneous features and associated with multiple valid labels simultaneously. Existing …

This paper addresses the problem of partial multi-label learning (PML), a challenging
weakly supervised learning framework, where each sample is associated with a candidate …

Partial multi-label learning (PML) is defined as the construction of robust multi-label
classification models from a training set where all instances are correlated with a …

Partial label learning (PLL) learns from training examples each associated with multiple
candidate labels, among which only one is valid. In recent years, benefiting from the strong …

The recent introduction of prompt tuning based on pre-trained vision-language models has
dramatically improved the performance of multi-label image classification. However, some …

Pre-trained vision-language models learn massive data to model unified representations of
images and natural languages, which can be widely applied to downstream machine …

Partial multi-label learning (PMLL) refers to a weakly-supervised classification problem,
where each instance is associated with a set of candidate labels, covering its ground-truth …