Benchmark datasets for visual recognition assume that data is uniformly distributed, while real-world datasets obey long-tailed distribution. Current approaches handle the long-tailed problem to transform the long-tailed dataset to uniform distribution by re-sampling or re-weighting strategies. These approaches emphasize the tail classes but ignore the hard examples in head classes, which result in performance degradation. In this paper, we propose a novel gradient harmonized mechanism with category-wise adaptive precision to decouple the difficulty and sample size imbalance in the long-tailed problem, which are correspondingly solved via intra- and inter-category balance strategies. Specifically, intra-category balance focuses on the hard examples in each category to optimize the decision boundary, while inter-category balance aims to correct the shift of decision boundary by taking each category as a unit. Extensive experiments demonstrate that the proposed method consistently outperforms other approaches on all the datasets.
翻译:用于直观识别的基准数据集假定数据分布一致,而真实世界数据集则服从于长尾分布。目前的方法通过重新抽样或重新加权战略处理将长尾数据集转换为统一分布的长期问题。这些方法强调尾类,但忽略头类的硬例子,从而导致性能退化。在本文件中,我们建议建立一个具有类别性适应精确度的新梯度统一机制,以区分长尾问题的难度和抽样大小不平衡,这些问题通过类别内和类别间平衡战略相应解决。具体地说,类别内平衡侧重于每个类别中的硬示例,以优化决定边界,而类别内平衡的目的是将每个类别作为一个单位来纠正决定边界的变动。广泛的实验表明,拟议的方法始终优于所有数据集上的其他方法。