Comparative Study of Distributed Deep Learning Tools on Supercomputers

Du, Xin; Kuang, Di; Yan, Ye; Li, Xinxin; Chen, Mengqiang; Du, Yunfei; Wu, Weigang

doi:10.1007/978-3-030-05051-1_9

Cited by 4 publications

(1 citation statement)

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“…It extracts useful local and global features from the input image by using a deep residual network (ResNet)[23,31] and feature pyramid network[22,24,98]. • Region proposal network.…”

mentioning

confidence: 99%

Human pose, hand and mesh estimation using deep learning: a survey

Toshpulatov

Lee

et al. 2022

J Supercomput

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Human pose estimation is one of the issues that have gained many benefits from using state-of-the-art deep learning-based models. Human pose, hand and mesh estimation is a significant problem that has attracted the attention of the computer vision community for the past few decades. A wide variety of solutions have been proposed to tackle the problem. Deep Learning-based approaches have been extensively studied in recent years and used to address several computer vision problems. However, it is sometimes hard to compare these methods due to their intrinsic difference. This paper extensively summarizes the current deep learning-based 2D and 3D human pose, hand and mesh estimation methods with a single or multi-person, single or double-stage methodology-based taxonomy. The authors aim to make every step in the deep learning-based human pose, hand and mesh estimation techniques interpretable by providing readers with a readily understandable explanation. The presented taxonomy has clearly illustrated current research on deep learning-based 2D and 3D human pose, hand and mesh estimation. Moreover, it also provided dataset and evaluation metrics for both 2D and 3DHPE approaches.

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“…It extracts useful local and global features from the input image by using a deep residual network (ResNet)[23,31] and feature pyramid network[22,24,98]. • Region proposal network.…”

mentioning

confidence: 99%