Human parsing is an important technology in human–robot interaction systems. At present, the distribution of multi-category human parsing datasets is unbalanced, and the samples present a long-tailed distribution, which directly affects the performance of human parsing. Meanwhile, the similarity between different categories leads the model to predict false parsing results. To solve the above problems, a general decoupled training framework called Decoupled Training framework based on Pixel Resampling (DTPR) was proposed to solve the long-tailed distribution, and a new sampling method named Pixel Resampling based on Accuracy distribution (PRA) for semantic segmentation was also proposed and applied to this decoupled training framework. The framework divides the training process into two phases, the first phase is to improve the model feature extraction ability, and the second phase is to improve the performance of the model on tail categories. The training framework was evaluated in MHPv2.0 and LIP datasets, and tested in both high-precision and real-time SOTA models. The MPA metric of model trained by DTPR in above two datasets increased by more than 6%, and the mIoU metric increased by more than 1% without changing the model structure.

中文翻译：

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用于人体解析的通用解耦训练框架

人体解析是人机交互系统中的一项重要技术。目前，多类人体解析数据集分布不均衡，样本呈现长尾分布，直接影响人体解析性能。同时，不同类别之间的相似性导致模型预测错误的解析结果。针对上述问题，提出了基于像素重采样的解耦训练框架（DTPR）来解决长尾分布的通用解耦训练框架，并提出了一种基于精度分布的像素重采样（PRA）的新采样方法进行语义分割也被提出并应用于这种解耦的训练框架。该框架将训练过程分为两个阶段，第一阶段是提高模型特征提取能力，第二阶段是提高模型在尾部类别上的性能。训练框架在 MHPv2.0 和 LIP 数据集中进行了评估，并在高精度和实时 SOTA 模型中进行了测试。DTPR训练的模型在上述两个数据集中的MPA指标提高了6%以上，mIoU指标在不改变模型结构的情况下提高了1%以上。