The human parsing task is dedicated to allocating pixel-level fine-grained semantic labels. However, recent solutions are limited to fully utilize the prior information (poses and edges) and the potential information of the image data, remaining problems with ambiguous boundaries, incomplete human parts, and redundant labels. To solve the above problems, we propose a novel Multi-view Stack Network (MVSN), which is constructed by three stacks with multiple views of features included parts, edges, and pre-segmentation. Meanwhile, a channel correlator is developed to acquire the correlation between local and global information better. Comprehensive experiments and corresponding results on three public datasets show that the proposed MVSN performs favorably against the state-of-the-art methods.

人工解析任务致力于分配像素级细粒度语义标签。然而，最近的解决方案仅限于充分利用图像数据的先验信息（姿势和边缘）和潜在信息，存在边界模糊、人体部分不完整和标签冗余的问题。为了解决上述问题，我们提出了一种新颖的多视图堆栈网络（MVSN），它由三个堆栈构成，具有多个特征视图，包括部分、边缘和预分割。同时，开发了信道相关器以更好地获取局部和全局信息之间的相关性。在三个公共数据集上的综合实验和相应结果表明，所提出的 MVSN 与最先进的方法相比表现良好。