Research on human parsing methods has attracted increasing attention in a wide range of applications. However, dataset imbalance is still a challenging problem in this task, which directly affects the performance of human parsing. There are different types of dataset imbalance problems. For example, the numbers of samples for various labels in a dataset may differ, the scales of objects identified by different labels may vary considerably, the differences between some heterogeneous label types may be much smaller than other cases, and in some extreme situations, images may be labeled incorrectly. In this paper, we propose a rebalanced model for imbalanced human parsing. Two innovative blocks are included in the model, i.e., a pre-bilateral awareness block and a combined-order statistics awareness block. The function of the former is to leverage the multiscale feature extractors to capture the changing scale information in an efficient way from the spatial space. Meanwhile, the function of the latter is to exploit the information of the feature distributions from the channel space. Furthermore, we propose an imbalance data-drop algorithm to simultaneously solve the mislabeling and small sample label weighting problems. Extensive experiments are conducted on three datasets, and the experimental results demonstrate that our method is able to solve the problem of data imbalance efficiently and obtain better human parsing performance.

人体解析方法的研究在广泛的应用中引起了越来越多的关注。但是，数据集不平衡仍然是此任务中的挑战性问题，它直接影响人工分析的性能。存在不同类型的数据集不平衡问题。例如，数据集中各种标签的样本数量可能会有所不同，不同标签所标识的对象的比例可能会相差很大，某些异构标签类型之间的差异可能会比其他情况小得多，在某些极端情况下，图像可能标签不正确。在本文中，我们提出了一种用于不平衡人体分析的重新平衡模型。该模型包含两个创新模块，即，双边预知模块和综合统计模块模块。前者的功能是利用多尺度特征提取器以有效的方式从空间空间捕获变化的尺度信息。同时，后者的功能是从信道空间中利用特征分布的信息。此外，我们提出了一种不平衡数据丢失算法来同时解决标签错误和小样本标签加权问题。对三个数据集进行了广泛的实验，实验结果表明我们的方法能够有效解决数据不平衡问题，并获得更好的人工解析性能。