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Recently, as an effective way of learning latent representations, contrastivelearning has been increasingly popular and successful in various domains. Thesuccess of constrastive learning in single-label classifications motivates usto leverage this learning framework to enhance distinctiveness for betterperformance in multi-label image classification. In this paper, we show that adirect application of contrastive learning can hardly improve in multi-labelcases. Accordingly, we propose a novel framework for multi-label classificationwith contrastive learning in a fully supervised setting, which learns multiplerepresentations of an image under the context of different labels. Thisfacilities a simple yet intuitive adaption of contrastive learning into ourmodel to boost its performance in multi-label image classification. Extensiveexperiments on two benchmark datasets show that the proposed framework achievesstate-of-the-art performance in the comparison with the advanced methods inmulti-label classification.

Multi-Label Image Classification with Contrastive Learning