标题： 长尾视觉识别的置换不变头尾特征融合 显示英文标题

标题： Long-Tailed Visual Recognition via Permutation-Invariant Head-to-Tail Feature Fusion

摘要： 长尾数据分布的不平衡给深度学习模型带来了重大挑战，导致它们倾向于关注头部类别而忽略尾部类别。 造成低识别准确率的两个关键因素是表示空间的退化和有偏的分类器，这些源于尾部类别中的语义信息不足。 为了解决这些问题，我们提出了一个名为排列不变且头尾特征融合（PI-H2T）的高度自适应方法。 PI-H2T 通过排列不变表示融合（PIF）来增强表示空间，从而产生更集中的特征和自动化的类别边界。 此外，它通过头尾融合（H2TF）从头部类别向尾部类别转移语义信息，调整有偏的分类器，提高尾部类别的多样性。 理论分析和实验表明，PI-H2T 优化了表示空间和决策边界。 其即插即用的设计确保可以无缝集成到现有方法中，为进一步性能提升提供了简单路径。 在长尾基准上的大量实验验证了 PI-H2T 的有效性。 显示英文摘要

摘要： The imbalanced distribution of long-tailed data presents a significant challenge for deep learning models, causing them to prioritize head classes while neglecting tail classes. Two key factors contributing to low recognition accuracy are the deformed representation space and a biased classifier, stemming from insufficient semantic information in tail classes. To address these issues, we propose permutation-invariant and head-to-tail feature fusion (PI-H2T), a highly adaptable method. PI-H2T enhances the representation space through permutation-invariant representation fusion (PIF), yielding more clustered features and automatic class margins. Additionally, it adjusts the biased classifier by transferring semantic information from head to tail classes via head-to-tail fusion (H2TF), improving tail class diversity. Theoretical analysis and experiments show that PI-H2T optimizes both the representation space and decision boundaries. Its plug-and-play design ensures seamless integration into existing methods, providing a straightforward path to further performance improvements. Extensive experiments on long-tailed benchmarks confirm the effectiveness of PI-H2T.