  Contrastive learning (CL) has shown impressive advances in image
representation learning in whichever supervised multi-class classification or
unsupervised learning. However, these CL methods fail to be directly adapted to
multi-label image classification due to the difficulty in defining the positive
and negative instances to contrast a given anchor image in multi-label
scenario, let the label missing one alone, implying that borrowing a
commonly-used way from contrastive multi-class learning to define them will
incur a lot of false negative instances unfavorable for learning. In this
paper, with the introduction of a label correction mechanism to identify
missing labels, we first elegantly generate positives and negatives for
individual semantic labels of an anchor image, then define a unique contrastive
loss for multi-label image classification with missing labels (CLML), the loss
is able to accurately bring images close to their true positive images and
false negative images, far away from their true negative images. Different from
existing multi-label CL losses, CLML also preserves low-rank global and local
label dependencies in the latent representation space where such dependencies
have been shown to be helpful in dealing with missing labels. To the best of
our knowledge, this is the first general multi-label CL loss in the
missing-label scenario and thus can seamlessly be paired with those losses of
any existing multi-label learning methods just via a single hyperparameter. The
proposed strategy has been shown to improve the classification performance of
the Resnet101 model by margins of 1.2%, 1.6%, and 1.3% respectively on three
standard datasets, MSCOCO, VOC, and NUS-WIDE. Code is available at
https://github.com/chuangua/ContrastiveLossMLML.
