Multi-label image classification aims to predict all possible labels in an
image. It is usually formulated as a partial-label learning problem, given the
fact that it could be expensive in practice to annotate all labels in every
training image. Existing works on partial-label learning focus on the case
where each training image is annotated with only a subset of its labels. A
special case is to annotate only one positive label in each training image. To
further relieve the annotation burden and enhance the performance of the
classifier, this paper proposes a new partial-label setting in which only a
subset of the training images are labeled, each with only one positive label,
while the rest of the training images remain unlabeled. To handle this new
setting, we propose an end-to-end deep network, PLMCL (Partial Label Momentum
Curriculum Learning), that can learn to produce confident pseudo labels for
both partially-labeled and unlabeled training images. The novel momentum-based
law updates soft pseudo labels on each training image with the consideration of
the updating velocity of pseudo labels, which help avoid trapping to
low-confidence local minimum, especially at the early stage of training in lack
of both observed labels and confidence on pseudo labels. In addition, we
present a confidence-aware scheduler to adaptively perform easy-to-hard
learning for different labels. Extensive experiments demonstrate that our
proposed PLMCL outperforms many state-of-the-art multi-label classification
methods under various partial-label settings on three different datasets.

本研究提出了一个新的部分标签学习的设置，其中只标注了训练图像的子集，每个图像只有一个正标签，其余训练图像保持未标注状态。通过使用端到端的深度神经网络 PLMCL，可以同时学习产生对部分标注和未标注的训练图像都具有信心的伪标签。通过考虑伪标签更新的速度，使用新的基于动量的法则来更新软伪标签，以帮助防止在早期训练阶段陷入低信心的局部最小值。此外，还提供了适应不同标签的逐渐递增学习的自适应 “自信度调度器”。实验结果表明，我们的 PLMCL 方法在三个不同数据集上的多标签分类中，表现比许多现有的基于多标签分类方法更好。