Multi-Label Image Classification with Contrastive Learning
This addresses the challenge of improving multi-label classification for computer vision applications, though it is incremental as it adapts existing contrastive learning methods to a specific domain.
The paper tackled the problem of applying contrastive learning to multi-label image classification, showing that direct application fails and proposing a novel supervised framework that learns multiple representations per image to achieve state-of-the-art performance on benchmark datasets.
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.