  Recently, as an effective way of learning latent representations, contrastive
learning has been increasingly popular and successful in various domains. The
success of constrastive learning in single-label classifications motivates us
to leverage this learning framework to enhance distinctiveness for better
performance in multi-label image classification. In this paper, we show that a
direct application of contrastive learning can hardly improve in multi-label
cases. Accordingly, we propose a novel framework for multi-label classification
with contrastive learning in a fully supervised setting, which learns multiple
representations of an image under the context of different labels. This
facilities a simple yet intuitive adaption of contrastive learning into our
model to boost its performance in multi-label image classification. Extensive
experiments on two benchmark datasets show that the proposed framework achieves
state-of-the-art performance in the comparison with the advanced methods in
multi-label classification.
