.png){kind=icon}
返回
End-to-End Instance-Level Human Parsing by Segmenting Persons
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2023.3260631.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Instance-level human parsing is aimed at separately partitioning the human body into different semantic parts for each individual, which remains a challenging task due to human appearance/pose variation, occlusion and complex backgrounds. Most state-of-the-art methods follow the parsing-by-detection paradigm, which relies on a trained detector to localize persons and then sequentially performs single-person parsing for each person. However, this paradigm is closely related to the detector, and the runtime is proportional to the number of persons in an image. In this paper, we present a novel detection-free framework for instance-level human parsing in an end-to-end manner. We decompose instance-level human parsing into two subtasks via a unified network: 1) semantic segmentation for pixel-level classification as a human part and 2) instance segmentation for mask-level classification as a person. The framework can directly predict the human-part semantic mask for all persons and binary masks for instance-level persons in parallel. The parsing result of each person can be acquired via a Hadamard product between the human-part semantic mask and the corresponding person's binary mask. Extensive experiments demonstrate that our proposed method performs favorably against state-of-the-art methods on the CIHP and MHP v2 datasets.
Keyword:
Instance-level human parsing
semantic segment- ation
instance segmentation
end-to-end training
期刊
IF:
9.7
论文数:
4.5K
被引数:
2.4W
机构
引用论文
Inflexibility of mental planning: A characteristic disorder with prefrontal lobe lesions?心理计划的僵化: 前额叶病变的特征性障碍？
PREAI
分享收藏
Using SEPIC Topology for Improving Power Factor in Distributed Power Supply Systems.png)
EPE Journal
EPE JournalIF0
PREAI
分享收藏
查看更多内容