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
GitHub https://github.com/soeaver/M2FP
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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