  Multi-label image classification presents a challenging task in many domains,
including computer vision and medical imaging. Recent advancements have
introduced graph-based and transformer-based methods to improve performance and
capture label dependencies. However, these methods often include complex
modules that entail heavy computation and lack interpretability. In this paper,
we propose Probabilistic Multi-label Contrastive Learning (ProbMCL), a novel
framework to address these challenges in multi-label image classification
tasks. Our simple yet effective approach employs supervised contrastive
learning, in which samples that share enough labels with an anchor image based
on a decision threshold are introduced as a positive set. This structure
captures label dependencies by pulling positive pair embeddings together and
pushing away negative samples that fall below the threshold. We enhance
representation learning by incorporating a mixture density network into
contrastive learning and generating Gaussian mixture distributions to explore
the epistemic uncertainty of the feature encoder. We validate the effectiveness
of our framework through experimentation with datasets from the computer vision
and medical imaging domains. Our method outperforms the existing
state-of-the-art methods while achieving a low computational footprint on both
datasets. Visualization analyses also demonstrate that ProbMCL-learned
classifiers maintain a meaningful semantic topology.
