  Semi-supervised multi-label learning (SSMLL) is a powerful framework for
leveraging unlabeled data to reduce the expensive cost of collecting precise
multi-label annotations. Unlike semi-supervised learning, one cannot select the
most probable label as the pseudo-label in SSMLL due to multiple semantics
contained in an instance. To solve this problem, the mainstream method
developed an effective thresholding strategy to generate accurate
pseudo-labels. Unfortunately, the method neglected the quality of model
predictions and its potential impact on pseudo-labeling performance. In this
paper, we propose a dual-perspective method to generate high-quality
pseudo-labels. To improve the quality of model predictions, we perform
dual-decoupling to boost the learning of correlative and discriminative
features, while refining the generation and utilization of pseudo-labels. To
obtain proper class-wise thresholds, we propose the metric-adaptive
thresholding strategy to estimate the thresholds, which maximize the
pseudo-label performance for a given metric on labeled data. Experiments on
multiple benchmark datasets show the proposed method can achieve the
state-of-the-art performance and outperform the comparative methods with a
significant margin.
