来自今天的爱可可AI前沿推介
[CV] Deep Learning for Human Parsing: A Survey
X Zhang, X Zhu, M Tang, Z Lei
[Chinese Academy of Sciences]
深度学习人体解析综述
要点:
-
概述了2022年前提出的基于深度学习的人体解析算法； -
提供了对人体解析方法的见解，包括网络架构、训练数据、主要贡献和限制； -
回顾了流行的人体解析数据集，并对所回顾的方法在流行基准上的性能进行了比较总结。
一句话总结:
全面概述了用于人体解析的最先进的深度学习方法，分为五个类别，包括对流行数据集、性能比较和未来研究方向的总结。
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.
论文链接：https://arxiv.org/abs/2301.12416
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