EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer

Neural architecture search has proven to be highly effective in the design of efficient convolutional neural networks that are better suited for mobile deployment than hand-designed networks. Hypothesizing that neural architecture search holds great potential for human pose estimation, we explore th...

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Main Authors: William McNally, Kanav Vats, Alexander Wong, John McPhee
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9559918/
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author William McNally
Kanav Vats
Alexander Wong
John McPhee
author_facet William McNally
Kanav Vats
Alexander Wong
John McPhee
author_sort William McNally
collection DOAJ
description Neural architecture search has proven to be highly effective in the design of efficient convolutional neural networks that are better suited for mobile deployment than hand-designed networks. Hypothesizing that neural architecture search holds great potential for human pose estimation, we explore the application of neuroevolution, a form of neural architecture search inspired by biological evolution, in the design of 2D human pose networks for the first time. Additionally, we propose a new weight transfer scheme that enables us to accelerate neuroevolution in a flexible manner. Our method produces network designs that are more efficient and more accurate than state-of-the-art hand-designed networks. In fact, the generated networks process images at higher resolutions using less computation than previous hand-designed networks at lower resolutions, allowing us to push the boundaries of 2D human pose estimation. Our base network designed via neuroevolution, which we refer to as EvoPose2D-S, achieves comparable accuracy to SimpleBaseline while being 50&#x0025; faster and <inline-formula> <tex-math notation="LaTeX">$12.7\times $ </tex-math></inline-formula> smaller in terms of file size. Our largest network, EvoPose2D-L, achieves new state-of-the-art accuracy on the Microsoft COCO Keypoints benchmark, is <inline-formula> <tex-math notation="LaTeX">$4.3\times $ </tex-math></inline-formula> smaller than its nearest competitor, and has similar inference speed. The code is publicly available at <uri>https://github.com/wmcnally/evopose2d</uri>.
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spelling doaj.art-0e2a76edec5e4497b8a8ae2b4ec6a0572022-12-22T03:12:46ZengIEEEIEEE Access2169-35362021-01-01913940313941410.1109/ACCESS.2021.31182079559918EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight TransferWilliam McNally0https://orcid.org/0000-0002-7187-7147Kanav Vats1Alexander Wong2https://orcid.org/0000-0001-5729-5899John McPhee3https://orcid.org/0000-0003-3908-9519Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, CanadaDepartment of Systems Design Engineering, University of Waterloo, Waterloo, ON, CanadaDepartment of Systems Design Engineering, University of Waterloo, Waterloo, ON, CanadaDepartment of Systems Design Engineering, University of Waterloo, Waterloo, ON, CanadaNeural architecture search has proven to be highly effective in the design of efficient convolutional neural networks that are better suited for mobile deployment than hand-designed networks. Hypothesizing that neural architecture search holds great potential for human pose estimation, we explore the application of neuroevolution, a form of neural architecture search inspired by biological evolution, in the design of 2D human pose networks for the first time. Additionally, we propose a new weight transfer scheme that enables us to accelerate neuroevolution in a flexible manner. Our method produces network designs that are more efficient and more accurate than state-of-the-art hand-designed networks. In fact, the generated networks process images at higher resolutions using less computation than previous hand-designed networks at lower resolutions, allowing us to push the boundaries of 2D human pose estimation. Our base network designed via neuroevolution, which we refer to as EvoPose2D-S, achieves comparable accuracy to SimpleBaseline while being 50&#x0025; faster and <inline-formula> <tex-math notation="LaTeX">$12.7\times $ </tex-math></inline-formula> smaller in terms of file size. Our largest network, EvoPose2D-L, achieves new state-of-the-art accuracy on the Microsoft COCO Keypoints benchmark, is <inline-formula> <tex-math notation="LaTeX">$4.3\times $ </tex-math></inline-formula> smaller than its nearest competitor, and has similar inference speed. The code is publicly available at <uri>https://github.com/wmcnally/evopose2d</uri>.https://ieeexplore.ieee.org/document/9559918/Artificial intelligencecomputer visionconvolutional neural networkdeep learninghuman pose estimationneural architecture search
spellingShingle William McNally
Kanav Vats
Alexander Wong
John McPhee
EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer
IEEE Access
Artificial intelligence
computer vision
convolutional neural network
deep learning
human pose estimation
neural architecture search
title EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer
title_full EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer
title_fullStr EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer
title_full_unstemmed EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer
title_short EvoPose2D: Pushing the Boundaries of 2D Human Pose Estimation Using Accelerated Neuroevolution With Weight Transfer
title_sort evopose2d pushing the boundaries of 2d human pose estimation using accelerated neuroevolution with weight transfer
topic Artificial intelligence
computer vision
convolutional neural network
deep learning
human pose estimation
neural architecture search
url https://ieeexplore.ieee.org/document/9559918/
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AT kanavvats evopose2dpushingtheboundariesof2dhumanposeestimationusingacceleratedneuroevolutionwithweighttransfer
AT alexanderwong evopose2dpushingtheboundariesof2dhumanposeestimationusingacceleratedneuroevolutionwithweighttransfer
AT johnmcphee evopose2dpushingtheboundariesof2dhumanposeestimationusingacceleratedneuroevolutionwithweighttransfer