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arXiv · 2107.11626

Multi-Label Image Classification with Contrastive Learning

Son D. Dao·Ethan Zhao·Dinh Phung·Jianfei Cai
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Abstract

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.

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BibTeXRIS

Son D. Dao, Ethan Zhao, Dinh Phung, Jianfei Cai. 2021-07-24. Multi-Label Image Classification with Contrastive Learning. https://arxiv.org/abs/2107.11626

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