Survey of human segmentation into parts using deep learning
Survey of human segmentation into parts using deep learning
Deep Learning Technique for Human Parsing: A Survey and Outlook
arXiv paper abstract https://arxiv.org/abs/2301.00394
arXiv PDF paper https://arxiv.org/pdf/2301.00394.pdf
Human parsing aims to partition humans in image or video into multiple pixel-level semantic parts.
In the last decade, it has gained significantly increased interest in the computer vision community and has been utilized in a broad range of practical applications, from security monitoring, to social media, to visual special effects, just to name a few.
Although deep learning-based human parsing solutions have made remarkable achievements, many important concepts, existing challenges, and potential research directions are still confusing.
... comprehensively review three core sub-tasks: single human parsing, multiple human parsing, and video human parsing, by introducing their respective task settings, background concepts, relevant problems and applications, representative literature, and datasets.
... also present quantitative performance comparisons of the reviewed methods on benchmark datasets.
... put forward a transformer-based human parsing framework, providing a high-performance baseline for follow-up research through universal, concise, and extensible solutions ...
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