Self-supervised learning (SSL) has achieved impressive results across several computer vision tasks, even rivaling supervised methods. However, its performance degrades on real-world datasets with long-tailed distributions due to difficulties in capturing inherent class imbalances. Although supervised long-tailed learning offers significant insights, the absence of labels in SSL prevents direct transfer of these strategies.To bridge this gap, we introduce Adaptive Paradigm Synergy (APS), a cross-paradigm objective that seeks to unify the strengths of both paradigms. Our approach reexamines contrastive learning from a spatial structure perspective, dynamically adjusting the uniformity of latent space structure through adaptive temperature tuning. Furthermore, we draw on a re-weighting strategy from supervised learning to compensate for the shortcomings of temperature adjustment in explicit quantity perception.Extensive experiments on commonly used long-tailed datasets demonstrate that APS improves performance effectively and efficiently. Our findings reveal the potential for deeper integration between supervised and self-supervised learning, paving the way for robust models that handle real-world class imbalance.

翻译：自监督学习（SSL）在多项计算机视觉任务中取得了令人瞩目的成果，甚至可与监督方法相媲美。然而，在具有长尾分布的真实世界数据集上，其性能会因难以捕捉固有的类别不平衡而下降。尽管监督式长尾学习提供了重要洞见，但SSL中标签的缺失阻碍了这些策略的直接迁移。为弥合这一差距，我们提出了自适应范式协同（APS），这是一种旨在统一两种范式优势的跨范式目标。我们的方法从空间结构视角重新审视对比学习，通过自适应温度调节动态调整潜在空间结构的均匀性。此外，我们借鉴监督学习中的重加权策略，以弥补温度调节在显式数量感知方面的不足。在常用长尾数据集上的大量实验表明，APS能有效且高效地提升性能。我们的研究揭示了监督学习与自监督学习之间更深层次整合的潜力，为构建能够处理真实世界类别不平衡的鲁棒模型开辟了道路。