  In multi-label classification, each training instance is associated with
multiple class labels simultaneously. Unfortunately, collecting the fully
precise class labels for each training instance is time- and labor-consuming
for real-world applications. To alleviate this problem, a novel labeling
setting termed \textit{Determined Multi-Label Learning} (DMLL) is proposed,
aiming to effectively alleviate the labeling cost inherent in multi-label
tasks. In this novel labeling setting, each training instance is associated
with a \textit{determined label} (either "Yes" or "No"), which indicates
whether the training instance contains the provided class label. The provided
class label is randomly and uniformly selected from the whole candidate labels
set. Besides, each training instance only need to be determined once, which
significantly reduce the annotation cost of the labeling task for multi-label
datasets. In this paper, we theoretically derive an risk-consistent estimator
to learn a multi-label classifier from these determined-labeled training data.
Additionally, we introduce a similarity-based prompt learning method for the
first time, which minimizes the risk-consistent loss of large-scale pre-trained
models to learn a supplemental prompt with richer semantic information.
Extensive experimental validation underscores the efficacy of our approach,
demonstrating superior performance compared to existing state-of-the-art
methods.
