  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.
