Contrastively enforcing distinctiveness for multi-label image classification

Son D. Dao, He Zhao, Dinh Phung, Jianfei Cai

Research output: Contribution to journalArticleResearchpeer-review

1 Citation (Scopus)

Abstract

Recently, as an effective way of learning latent representations, contrastive learning has been increasingly popular and successful in various domains. The success of contrastive 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 introduces a simple yet intuitive adaption of contrastive learning into our model to boost its performance in multi-label image classification. Extensive experiments on four benchmark datasets show that the proposed framework achieves state-of-the-art performance in the comparison with the advanced methods in multi-label classification.

Original languageEnglish
Article number126605
Number of pages12
JournalNeurocomputing
Volume555
DOIs
Publication statusPublished - 28 Oct 2023

Keywords

  • Contrastive learning
  • Multi-label classification
  • Neural networks

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