Supervised contrastive learning has achieved remarkable success by leveraging label information; however, determining positive samples in multi-label scenarios remains a critical challenge. In multi-label supervised contrastive learning (MSCL), multi-label relations are not yet fully defined, leading to ambiguity in identifying positive samples and formulating contrastive loss functions to construct the representation space. To address these challenges, we: (i) systematically formulate multi-label relations in MSCL, (ii) propose a novel Similarity-Dissimilarity Loss, which dynamically re-weights samples based on similarity and dissimilarity factors, (iii) further provide theoretically grounded proofs for our method through rigorous mathematical analysis that supports the formulation and effectiveness, and (iv) offer a unified form and paradigm for both single-label and multi-label supervised contrastive loss. We conduct experiments on both image and text modalities and further extend the evaluation to the medical domain. The results show that our method consistently outperforms baselines in comprehensive evaluations, demonstrating its effectiveness and robustness.

翻译：监督对比学习通过利用标签信息取得了显著成功，但在多标签场景中确定正样本仍是关键挑战。多标签监督对比学习中，多标签关系尚未被充分定义，导致在识别正样本和构建表示空间的对比损失函数时存在歧义。为解决这些问题，我们：（i）系统性地定义了多标签监督对比学习中的多标签关系；（ii）提出了一种新型的相似-不相似损失，该损失基于相似因子与不相似因子对样本进行动态重新加权；（iii）通过严格的数学分析为所提方法提供了理论支撑的证明，验证了其表述与有效性；（iv）为单标签与多标签监督对比损失提供了统一的形式化框架。我们在图像和文本两种模态上开展实验，并将评估范围扩展至医学领域。结果表明，我们的方法在综合评估中持续优于基线方法，展现了其有效性与鲁棒性。