In real-world data, long-tailed data distribution is common, making it challenging for models trained on empirical risk minimisation to learn and classify tail classes effectively. While many studies have sought to improve long tail recognition by altering the data distribution in the feature space and adjusting model decision boundaries, research on the synergy and corrective approach among various methods is limited. Our study delves into three long-tail recognition techniques: Supervised Contrastive Learning (SCL), Rare-Class Sample Generator (RSG), and Label-Distribution-Aware Margin Loss (LDAM). SCL enhances intra-class clusters based on feature similarity and promotes clear inter-class separability but tends to favour dominant classes only. When RSG is integrated into the model, we observed that the intra-class features further cluster towards the class centre, which demonstrates a synergistic effect together with SCL's principle of enhancing intra-class clustering. RSG generates new tail features and compensates for the tail feature space squeezed by SCL. Similarly, LDAM is known to introduce a larger margin specifically for tail classes; we demonstrate that LDAM further bolsters the model's performance on tail classes when combined with the more explicit decision boundaries achieved by SCL and RSG. Furthermore, SCL can compensate for the dominant class accuracy sacrificed by RSG and LDAM. Our research emphasises the synergy and balance among the three techniques, with each amplifying the strengths of the others and mitigating their shortcomings. Our experiment on long-tailed distribution datasets, using an end-to-end architecture, yields competitive results by enhancing tail class accuracy without compromising dominant class performance, achieving a balanced improvement across all classes.

翻译：在现实世界数据中，长尾数据分布普遍存在，这使得基于经验风险最小化训练的模型难以有效学习和分类尾部类别。尽管许多研究试图通过改变特征空间中的数据分布和调整模型决策边界来改进长尾识别，但关于多种方法间协同作用与校正策略的研究仍然有限。本研究深入探讨了三种长尾识别技术：监督对比学习（SCL）、稀有类别样本生成器（RSG）以及标签分布感知间隔损失（LDAM）。SCL基于特征相似性增强类内聚类并促进清晰的类间分离性，但往往仅对主导类别有利。当RSG集成到模型中时，我们观察到类内特征进一步向类中心聚集，这体现了与SCL增强类内聚类原理的协同效应。RSG生成新的尾部特征，并补偿被SCL挤压的尾部特征空间。类似地，已知LDAM会专门为尾部类别引入更大的间隔；我们证明当LDAM与SCL和RSG实现的更明确决策边界结合时，能进一步提升模型在尾部类别上的性能。此外，SCL可以补偿RSG和LDAM所牺牲的主导类别准确率。我们的研究强调了三种技术之间的协同与平衡关系，每种技术都能放大其他技术的优势并弥补其不足。通过在长尾分布数据集上使用端到端架构进行实验，我们在不损害主导类别性能的前提下提升了尾部类别准确率，实现了所有类别的均衡改进，取得了具有竞争力的结果。