Abstract
Human parsing is a key topic in image processing with many applications, such as surveillance analysis, human-robot interaction, person search, and clothing category classification, among many others. Recently, due to the success of deep learning in computer vision, there are a number of works aimed at developing human parsing algorithms using deep learning models. As methods have been proposed, a comprehensive survey of this topic is of great importance. In this survey, we provide an analysis of state-of-the-art human parsing methods, covering a broad spectrum of pioneering works for semantic human parsing. We introduce five insightful categories: (1) structure-driven architectures exploit the relationship of different human parts and the inherent hierarchical structure of a human body, (2) graph-based networks capture the global information to achieve an efficient and complete human body analysis, (3) context-aware networks explore useful contexts across all pixel to characterize a pixel of the corresponding class, (4) LSTM-based methods can combine short-distance and long-distance spatial dependencies to better exploit abundant local and global contexts, and (5) combined auxiliary information approaches use related tasks or supervision to improve network performance. We also discuss the advantages/disadvantages of the methods in each category and the relationships between methods in different categories, examine the most widely used datasets, report performances, and discuss promising future research directions in this area.
Abstract (translated)
人类分词是图像处理中一个重要的主题,有许多应用,例如监控分析、人机互动、人名搜索和服装分类等。最近,由于深度学习在计算机视觉中的成功,有许多工作旨在使用深度学习模型开发人类分词算法。由于提出了方法,这是一个非常重要全面的调查话题。在这个调查中,我们将提供最先进的人类分词方法的分析,涵盖了语义人类分词的早期工作广泛的范围。我们介绍了五个深刻的分类:(1)结构驱动的建筑学利用不同人类部件之间的关系和人体固有的层次结构;(2)基于图的神经网络捕捉全球信息,实现高效的完整的人体分析;(3)具有上下文意识的网络在所有像素探索有用的上下文,以确定相应的类别像素;(4)基于LSTM的方法可以结合短距离和长距离空间依赖,更好地利用丰富的本地和全球上下文;(5)综合辅助信息方法使用相关任务或监督来提高网络性能。我们还讨论了每种方法的优点和缺点,以及不同方法之间的区别,研究了最常用的数据集,报告性能,并讨论这一领域有前景的未来的研究方向。
URL
https://arxiv.org/abs/2301.12416
