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Heterogeneous Multi-task Learning for Human Pose Estimation with Deep Convolutional Neural Network
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DOI：10.1007/s11263-014-0767-8.png)
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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.
Keywords:
Human Pose Estimation
Deep Learning
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