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International Journal of Computer Vision

Erschienen in:

01.05.2015

Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network

verfasst von: Sijin Li, Zhi-Qiang Liu, Antoni B. Chan

Erschienen in: International Journal of Computer Vision | Ausgabe 1/2015

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Abstract

We propose a heterogeneous multi-task learning framework for human pose estimation from monocular images using a deep convolutional neural network. In particular, we simultaneously learn a human pose regressor and sliding-window body-part and joint-point detectors in a deep network architecture. We show that including the detection tasks helps to regularize the network, directing it to converge to a good solution. We report competitive and state-of-art results on several datasets. We also empirically show that the learned neurons in the middle layer of our network are tuned to localized body parts.

Vorheriger Artikel Stacked Predictive Sparse Decomposition for Classification of Histology Sections

Nächster Artikel Discriminative Deep Face Shape Model for Facial Point Detection

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The full results can be viewed at http://visal.cs.cityu.edu.hk/research/hmlpe-demo/.

As pointed out in (Hara and Chellappa 2013; Pishchulin et al. 2012), the code in the Buffy toolkit does not compute PCP correctly.

Since we have different definitions of torso and head parts, we do not show the evaluation of these parts here.

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Titel: Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
verfasst von: Sijin Li
Zhi-Qiang Liu
Antoni B. Chan
Publikationsdatum: 01.05.2015
Verlag: Springer US
Erschienen in: International Journal of Computer Vision / Ausgabe 1/2015
Print ISSN: 0920-5691
Elektronische ISSN: 1573-1405
DOI: https://doi.org/10.1007/s11263-014-0767-8

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